@article{2-s2.0-85204144991,

title = {Assessing Future Agricultural Vulnerability in Kashmir Valley: Mid- and Late-Century Projections Using SSP Scenarios},

author = { Ma. Farooq and Su.K. Singh and S. Kanga and G. Meraj and F. Mushtaq and B. Ðurin and Q.B. Pham and J.D. Hunt},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204144991&doi=10.3390%2fsu16177691&partnerID=40&md5=7d4259fb5c210dd6374f1e1f5acd729c},

doi = {10.3390/su16177691},

issn = {20711050},

year  = {2024},

date = {2024-01-01},

journal = {Sustainability (Switzerland)},

volume = {16},

number = {17},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {The fragile environment of the Himalayan region is prone to natural hazards, which are intensified by climate change, leading to food and livelihood insecurity for inhabitants. Therefore, building resilience in the most dominant livelihood sector, i.e., the agricultural sector, has become a priority in development and planning. To assess the perils induced by climate change on the agriculture sector in the ecologically fragile region of Kashmir Valley, a study has been conducted to evaluate the risk using the Intergovernmental Panel on Climate Change (IPCC) framework. The risk index has been derived based on socioeconomic and ecological indicators for risk determinants, i.e., vulnerability, hazard, and exposure. Furthermore, the study also evaluated the future risk to the agriculture sector under changing climatic conditions using Shared Socioeconomic Pathways (SSPs) for SSP2-4.5 and SSP5-8.5 at mid- and late-century timescales. It was observed that districts such as Bandipora (0.59), Kulgam (0.56), Ganderbal (0.56), and Kupwara (0.54) are most vulnerable due to drivers like low per capita income, yield variability, and areas with >30% slope. Shopian and Srinagar were found to be the least vulnerable due to adaptive capacity factors like livelihood diversification, crop diversification, percentage of tree crops, and percentage of agriculture labor. In terms of the Risk index, the districts found to be at high risk are Baramulla (0.19), Pulwama (0.16), Kupwara (0.15), and Budgam (0.13). In addition, the findings suggested that the region would experience a higher risk of natural hazards by the mid- (MC) and end-century (EC) due to the projected increase in temperature with decreasing precipitation, which would have an impact on crop yields and the livelihoods of farmers in the region. © 2024 by the authors.},

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}

@article{2-s2.0-85200943339,

title = {Satellite and Model-Based Estimation of Crop Water Requirement of Major Irrigated Crops in the Koga Irrigation Scheme, Ethiopia},

author = { A.E. Tiruye and P. Ditthakit and Q.B. Pham and W. Wipulanusat and U. Weesakul and S. Thongkao and N.L. Kushwaha},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200943339&doi=10.30919%2fes1155&partnerID=40&md5=54e9700e4247502e3c8a300d134d0a88},

doi = {10.30919/es1155},

issn = {2576988X},

year  = {2024},

date = {2024-01-01},

journal = {Engineered Science},

volume = {30},

publisher = {Engineered Science Publisher},

abstract = {An adequate supply of water and nutrients is vital in crop cultivation to conserve water, prevent shortages, minimize production losses, and curb excessive utilization of water resources. This study aimed to estimate the water demand of major irrigated crops using open-access portals, such as satellite and reanalysis datasets within the Koga irrigation scheme. We collected climate data from the local weather station, WaPOR, and ERA5-Land open-source datasets. The Koga Irrigation Development Office provided the field data for the entire irrigation system. The Modified Penman-Monteith equation was employed to compute the reference evapotranspiration. The single-crop coefficient approach was applied to determine crop evapotranspiration (ETC). The result indicated a strong correlation between the measured and predicted values. Additionally, it was also found that the ETC tended to peak during the mid-stage of the crops, and a robust relationship between the measured and estimated values was examined on a monthly scale. The seasonal average water requirements for wheat were 532 mm, 510 mm, and 542 mm, whereas maize had mean values of 603 mm, 589 mm, and 636 mm. Potato water needs averaged 549 mm, 529 mm, and 564 mm, and for tomatoes, the water requirements were 540 mm, 532 mm, and 583mm based on observed, satellite, and model-based estimations, respectively. Remarkably, the maize crop consistently had the highest ETc value across all estimation scenarios, indicating that maize is more susceptible to water stress, has a longer growing season, a large leaf area index when mature, and transpires at a higher rate than wheat, potato, and tomato. According to our research, the model-based estimation was 5.9 to 8.7% and 1.9 to 7.4% higher than the satellite-based estimates and the measured values, respectively. In conclusion, our research highlights the importance of utilizing satellite and model-based data to calculate crop evapotranspiration in irrigation schemes and benefits decision-makers, water managers, agronomists, stakeholders, and irrigation operators. © Engineered Science Publisher LLC 2024.},

note = {1},

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pubstate = {published},

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}

@article{2-s2.0-85193959602,

title = {Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches},

author = { V.K. Rana and Q.B. Pham and F. Granata and F. Di Nunno and T.D. Dang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193959602&doi=10.1016%2fj.asr.2024.05.001&partnerID=40&md5=498825d3f25b7f22045125096356fce4},

doi = {10.1016/j.asr.2024.05.001},

issn = {02731177},

year  = {2024},

date = {2024-01-01},

journal = {Advances in Space Research},

volume = {74},

number = {3},

pages = {1140-1163},

publisher = {Elsevier Ltd},

abstract = {Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, by integrating various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE, and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE, and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (Cv) metrics. The findings show that a PCA-based technique with SVM is marginally superior (based on the overall accuracy and Kappa coefficient with a value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the Cv metric. The SVM algorithm with a Cv of 1.25 demonstrated the best performance, followed by the RF algorithm with Cv of 1.5, and the MLE algorithm with Cv of 2.5. The worst performing algorithm among all is found to be ISO with Cv of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area. © 2024 COSPAR},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85198916355,

title = {Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment},

author = { C. Singha and V.K. Rana and Q.B. Pham and D.C. Nguyen and E.B. Łupikasza},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198916355&doi=10.1007%2fs11356-024-34286-7&partnerID=40&md5=4e77b4b8c382030bea9a4c1d165b0198},

doi = {10.1007/s11356-024-34286-7},

issn = {09441344},

year  = {2024},

date = {2024-01-01},

journal = {Environmental Science and Pollution Research},

volume = {31},

number = {35},

pages = {48497-48522},

publisher = {Springer},

abstract = {Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management. © The Author(s) 2024.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85191613022,

title = {Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network},

author = { Q.B. Pham and S.A. Ali and F. Parvin and V. van On and L. Mohd Sidek and B. Ðurin and V. Cetl and S. Šamanović and N.M. Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191613022&doi=10.1016%2fj.asr.2024.03.027&partnerID=40&md5=a7d02e76db581da686e4571599a03c7d},

doi = {10.1016/j.asr.2024.03.027},

issn = {02731177},

year  = {2024},

date = {2024-01-01},

journal = {Advances in Space Research},

volume = {74},

number = {1},

pages = {17-47},

publisher = {Elsevier Ltd},

abstract = {The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. © 2024},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85195823967,

title = {Spatiotemporal Variability of Gross Primary Productivity in Türkiye: A Multi-Source and Multi-Method Assessment},

author = { E.E. Başakın and P.C. Stoy and M.C. Demirel and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195823967&doi=10.3390%2frs16111994&partnerID=40&md5=6bec8c48824a754c9ff67f24a8adeb7b},

doi = {10.3390/rs16111994},

issn = {20724292},

year  = {2024},

date = {2024-01-01},

journal = {Remote Sensing},

volume = {16},

number = {11},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Türkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal–Wallis and Mann–Whitney U methods, and long-term trends were analyzed using Modified Mann–Kendall (MMK), innovative trend analysis (ITA), and empirical mode decomposition (EMD). Our results show that at least one GPP product significantly differs from the others over the seven geographic regions of Türkiye (χ2 values of 50.8; 21.9; 76.9; 42.6; 149; 34.5; and 168; p < 0.05), and trend analyses reveal a significant increase in GPP from all satellite-based products over the latter half of the study period. Throughout the year, the average number of months in which each dataset showed significant increases across all study regions are 6.7, 8.1, 5.9, 9.6, and 8.7 for MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2, respectively. The ITA and EMD methods provided additional insight into the MMK test in both visualizing and detecting trends due to their graphical techniques. Overall, the GPP products investigated here suggest ‘greening’ for Türkiye, consistent with the findings from global studies, but the use of different statistical approaches and satellite-based GPP estimates creates different interpretations of how these trends have emerged. Ground stations, such as eddy covariance towers, can help further improve our understanding of the carbon cycle across the diverse ecosystem of Türkiye. © 2024 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85193816981,

title = {Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network},

author = { San.K. Gupta and P.G.S. Kumar and G. Kishore and R. Ali and N.A. Al-Ansari and D.K. Vishwakarma and A. Kuriqi and Q.B. Pham and O. Kisi and S. Heddam and M.A. Mattar},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193816981&doi=10.1007%2fs13201-024-02195-2&partnerID=40&md5=9d245cb771b85b96977dbc310cebe216},

doi = {10.1007/s13201-024-02195-2},

issn = {21905487},

year  = {2024},

date = {2024-01-01},

journal = {Applied Water Science},

volume = {14},

number = {6},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis. © The Author(s) 2024.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85192942341,

title = {Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco},

author = { M. Aghad and M. Mohamed and M. Sadiki and Q.B. Pham and J. Alkarkouri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192942341&doi=10.1007%2fs10661-024-12711-1&partnerID=40&md5=143320754f3bfb7c4a1f9e1d4509c3fd},

doi = {10.1007/s10661-024-12711-1},

issn = {01676369},

year  = {2024},

date = {2024-01-01},

journal = {Environmental Monitoring and Assessment},

volume = {196},

number = {6},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction’s accuracy because the existing sites used in the validation’s process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85190142185,

title = {A novel additive regression model for streamflow forecasting in German rivers},

author = { F. Granata and F. Di Nunno and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190142185&doi=10.1016%2fj.rineng.2024.102104&partnerID=40&md5=cf1079482ed239c87524eb2fac733d4d},

doi = {10.1016/j.rineng.2024.102104},

issn = {25901230},

year  = {2024},

date = {2024-01-01},

journal = {Results in Engineering},

volume = {22},

publisher = {Elsevier B.V.},

abstract = {Forecasting streamflows, essential for flood mitigation and the efficient management of water resources for drinking, agriculture and hydroelectric power generation, presents a formidable challenge in most real-world scenarios. In this study, two models, the first based on the Additive Regression of Radial Basis Function Neural Networks (AR-RBF) and the second based on the stacking with the Pace Regression of the Multilayer Perceptron and Random Forest (MLP-RF-PR), were compared for the prediction of short-term (1–3 days ahead) and medium-term (7 days ahead) daily streamflow rates of three different rivers in Germany: the Elbe River at Wittenberge, the Leine River at Herrenhausen, and the Saale River at Hof. The lagged values of streamflow rate, precipitation and temperature were considered for the modeling. Moreover, the Bayesian Optimization (BO) algorithm was used to assess the optimal number of lagged values and hyperparameters. Both models showed accurate predictions for short-term forecasting, with R2 for 1-day ahead predictions ranging from 0.939 to 0.998 for AR-RBF and from 0.930 to 0.996 for MLP-RF-PR, while MAPE ranged from 2.02 % to 8.99 % for AR-RBF and from 2.14 % to 9.68 % for MLP-RF-PR, when exogeneous variables were included. As the forecast horizon increased, a reduction in forecasting accuracy was observed. However, both models could still predict the overall flow pattern, even for 7-day-ahead predictions, with R2 ranging from 0.772 to 0.871 for AR-RBF and from 0.703 to 0.840 for MLP-RF-PR, while MAPE ranged from 10.60 % to 20.45 % for AR-RBF and from 10.44 % to 19.65 % for MLP-RF-PR. Overall, the outcomes of this study suggest that both AR-RBF and MLP-RF-PR can be reliable tools for the short- and medium-term streamflow rate prediction, requiring a short number of parameters to be optimized, making them easy to implement while reducing the calculation time required. © 2024 The Authors},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85186583861,

title = {Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities},

author = { Vij. Kumar and K.V. Sharma and Q.B. Pham and A.K. Srivastava and C. Bogireddy and S.M. Yadav},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186583861&doi=10.1007%2fs00704-024-04914-w&partnerID=40&md5=514c5ff9b8cc8fe69fb7ddbdb99440e4},

doi = {10.1007/s00704-024-04914-w},

issn = {0177798X},

year  = {2024},

date = {2024-01-01},

journal = {Theoretical and Applied Climatology},

volume = {155},

number = {6},

pages = {4251-4288},

publisher = {Springer},

abstract = {The use of remote sensing for monitoring and managing droughts is examined in this review study. Drought has a significant impact on how water resources are managed and agricultural production is produced, and remote sensing is a vital technique for assessing and monitoring the severity of drought. A number of remote sensing data sources are discussed in the paper; including precipitation, groundwater and surface water storage, soil moisture, land surface temperature, evaporation, and agricultural indicators. With the use of these data sources, drought indices and indicators that measure the severity and spatiotemporal fluctuations of the drought may be developed. The novel approach of this review study emphasizes the benefits of using remote sensing to gain a full understanding of drought dynamics and to accurately capture fine-scale fluctuations in drought conditions. However, the study also highlights certain limitations, including issues related to data accessibility, data interpretation, and validation difficulties. It emphasizes the significance of using remote sensing to promote the developing policies and strategies to enhance drought resilience and adaptation. The importance of continuous research, technical development, and stakeholder cooperation in order to fully realize remote sensing's promise for tackling the complex problems associated with drought and promoting sustainable water resource management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85194251547,

title = {A Combined Seasonal Mann–Kendall and Innovative Approach for the Trend Analysis of Streamflow Rate in Two Croatian Rivers},

author = { M.B. Yıldız and F. Di Nunno and B. Ðurin and Q.B. Pham and G. de Marinis and F. Granata},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194251547&doi=10.3390%2fw16101422&partnerID=40&md5=c43d02c449aba04416994aa7da3115c8},

doi = {10.3390/w16101422},

issn = {20734441},

year  = {2024},

date = {2024-01-01},

journal = {Water (Switzerland)},

volume = {16},

number = {10},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {Climate change profoundly impacts hydrological systems, particularly in regions such as Croatia, which is renowned for its diverse geography and climatic variability. This study examined the effect of climate change on streamflow rates in two Croatian rivers: Bednja and Gornja Dobra. Using seasonal Mann–Kendall (MK) tests, overall streamflow trends were evaluated. Additionally, innovative polygon trend analysis (IPTA), innovative visualization for innovative trend analysis (IV-ITA), and Bayesian changepoint detection and time series decomposition (BEAST) algorithms were used to assess the trends’ magnitudes and transitions. The seasonal MK analysis identified significant decreasing trends, primarily during summer. The results of IPTA and IV-ITA revealed consistent decreasing trends throughout most months, with a notable increase in September, especially at high flow values. The rivers’ behavior differed between the first and second halves of the month. BEAST analysis detected abrupt changes, including earlier shifts (1951–1968) in the Bednja and more recent ones (2013–2015) in both the Bednja and, to a lesser extent, the Gornja Dobra rivers. This comprehensive approach enhances our understanding of long-term streamflow trends and short-term fluctuations induced by climate change. © 2024 by the authors.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85191714250,

title = {Effectiveness of Integrating Ensemble-Based Feature Selection and Novel Gradient Boosted Trees in Runoff Prediction: A Case Study in Vu Gia Thu Bon River Basin, Vietnam},

author = { O. Aiyelokun and Q.B. Pham and O. Aiyelokun and N.T.T. Linh and T. Roy and D. Tran Anh and E.B. Łupikasza},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191714250&doi=10.1007%2fs00024-024-03486-0&partnerID=40&md5=aa3029c673e350dcb8ceca66f6245985},

doi = {10.1007/s00024-024-03486-0},

issn = {00334553},

year  = {2024},

date = {2024-01-01},

journal = {Pure and Applied Geophysics},

volume = {181},

number = {5},

pages = {1725-1744},

publisher = {Birkhauser},

abstract = {Traditional rainfall-runoff modeling techniques require large datasets and often an exhaustive calibration process, which is challenging, especially in poorly-gauged basins and resource-limited settings. Therefore, it is necessary to examine new ways of constructing predictive models for runoff that can achieve satisfactory results, while also minimizing the data requirement and model construction time. In this study, the effectiveness of integrating the Random Forest (RF) as an important feature identifier with novel gradient boosted trees to achieve satisfactory results was examined for two adjacent catchments in Vietnam. Antecedent daily runoff in combination with daily and one-day antecedent rainfall was found to significantly influence the runoff at the outlet of the catchments. Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) were effective in predicting day-ahead runoff. For instance, CatBoost with NSE, d, r, and R2 values of 0.92, 0.98, 0.96, and 0.92, respectively, and XGBoost with NSE, d, r, and R2 values of 0.91, 0.98, 0.96, and 0.92, respectively, are well suited for predicting runoff. A comparative analysis of their results with previous studies revealed that the models were very effective since they were able to better reduce generalization errors at different calibration and validation phases. This study presents the integration of RF and gradient boosted trees as a simplified alternative to computationally expensive and data-intensive physically-based rainfall-runoff models. The practitioners can build upon the experimentation presented in this study to minimize the computational time requirement, construction process complexity, and data requirement, which are often serious constraints in physically-based rainfall-runoff modeling. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85184858330,

title = {Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand},

author = { P.S. Tulla and P.G.S. Kumar and D.K. Vishwakarma and Ro. Kumar and A. Kuriqi and N.L. Kushwaha and J. Rajput and A. Srivastava and Q.B. Pham and K.C. Panda and O. Kisi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184858330&doi=10.1007%2fs00704-024-04862-5&partnerID=40&md5=cdef8fa7bdb542004666eba2e870955b},

doi = {10.1007/s00704-024-04862-5},

issn = {0177798X},

year  = {2024},

date = {2024-01-01},

journal = {Theoretical and Applied Climatology},

volume = {155},

number = {5},

pages = {4023-4047},

publisher = {Springer},

abstract = {Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (i.e.; Bino watershed; Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000–2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.},

note = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85194705097,

title = {Assessing Water Consumption Pattern and Delivery Irrigation Performance Indicators Using the WaPOR Portal Under Data-Limited Conditions, Ethiopia},

author = { A.E. Tiruye and P. Ditthakit and Q.B. Pham and W. Wipulanusat and U. Weesakul and S. Thongkao},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194705097&doi=10.30919%2fes1046&partnerID=40&md5=d4dd1b61afd574ff9ce8574d915affe5},

doi = {10.30919/es1046},

issn = {2576988X},

year  = {2024},

date = {2024-01-01},

journal = {Engineered Science},

volume = {28},

publisher = {Engineered Science Publisher},

abstract = {Enhancing water use efficiency hinges on improving water consumption and the performance of irrigation systems. Nevertheless, challenges such as limited access to historical and current data, difficulty in securing continuous data, conflicts, the inability to cover vast areas, and high costs impede the monitoring and assessment of irrigation performance. This study aims to evaluate the water consumption patterns and irrigation performance indicators using open-access datasets. The study demonstrated the actual evapotranspiration and irrigation performance indicators, including water productivity, equity, adequacy, relative water deficit, and beneficial fraction. QGIS and ArcGIS were used to conduct spatiotemporal analysis on the locations of irrigated fields (lower; middle; and upper) across the Koga irrigation scheme. The WaPOR datasets on water, climate, and land, with a spatial resolution of 30 m and real-time temporal resolution, were used for the analysis of the irrigation scheme. The findings indicated that in the 2018 irrigation season, the minimum values for actual evapotranspiration, transpiration, biomass, and yield were 439 mm, 326 mm, 5.9 t ha-1, and 2.8 t ha-1, respectively. The estimated delivery performance indicators were categorized as poor for adequacy and relative water deficit, fair for equity, and acceptable for beneficial fraction. These outcomes provide insights into the features of irrigators and the overall trends in irrigation systems. The spatial and temporal analysis of delivery performance indicators highlighted that agronomic practices, management, and operations are the factors contributing to water scarcity in the irrigation scheme. In conclusion, our findings highlight that open-source data offers valuable insights for evaluating and overseeing water consumption and the performance of irrigation schemes in data-scarce regions. © Engineered Science Publisher LLC 2024.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85183448913,

title = {Drought and aridity trends on the Algerian steppe},

author = { M. Oubadi and M. Faci and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183448913&doi=10.1007%2fs00704-024-04865-2&partnerID=40&md5=40a74321913cbb88df2f5c006814c25d},

doi = {10.1007/s00704-024-04865-2},

issn = {0177798X},

year  = {2024},

date = {2024-01-01},

journal = {Theoretical and Applied Climatology},

volume = {155},

number = {3},

pages = {1541-1551},

publisher = {Springer},

abstract = {Arid regions are characterized by the fragility of their ecosystems, which are very vulnerable to climate change. The increase in aridity in these regions makes them more exposed to drought with serious consequences. This contribution analyzes the trend of aridity and drought in the Algerian steppes, during the period 1951–2022. Robust statistical tests (Mann–Kendall and Sen) and the Reconnaissance Drought Index (RDI) are used. Monthly rainfall data and monthly average temperature as well as potential evapotranspiration (PET) data from the Climatic Research Unit (CRU), characterized by a spatial resolution of 0.5°, were used. The results showed an increase in annual aridity, which led to an expansion of drylands. The change was characterized by the transition of 9.20% of the steppe surface from the semi-arid class to the arid class. At the same time, the entire subhumid class (1.67%) made the transition to the semi-arid class. On the other hand, a significant tendency towards drought over most of the steppe territory (81%) was recorded. The analysis of the results revealed the influence of precipitations and air temperatures in this enlargement. These changes have contributed to the degradation of the environment and natural resources and to an ecological and socio-economic imbalance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85176379371,

title = {Application of the ITA approach to analyze spatio-temporal trends in monthly maximum rainfall categories in the Vu Gia-Thu Bon, Vietnam},

author = { A. Karmaoui and A. Elouissi and E.B. Łupikasza and Q.B. Pham and A. Harizia and S. Fellah},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176379371&doi=10.1007%2fs00704-023-04702-y&partnerID=40&md5=2954080a508885781fc4ccb95f4e5e3b},

doi = {10.1007/s00704-023-04702-y},

issn = {0177798X},

year  = {2024},

date = {2024-01-01},

journal = {Theoretical and Applied Climatology},

volume = {155},

number = {2},

pages = {1467-1491},

publisher = {Springer},

abstract = {This study aims to investigate the trend behavior of monthly maximum in daily rainfall categories in the Vu Gia-Thu Bon river basin located in central Vietnam. Daily maximum rainfall series from 12 rainfall stations for the period 1979–2018 were utilized to characterize six categories of the intensity of daily maximum rainfall: light (0–4 mm/day; category A), mild-moderate (4–16 mm/day; category B), moderate-heavy (16–32 mm/day; category C1), heavy (32–64 mm/day; category C2), heavy-torrential (64–128 mm/day; category D1), and torrential (≥ 128 mm/day; category D2). The new approach of the Innovative Trends Analysis was then applied to the six classified categories. The results revealed that category B had a dominant increasing trend (32% of rain events) for all the stations in January (5.85%) and February (3.44%). In March and April, category A was dominant with 45% and 20%, respectively. In July, category C1 was dominant with 25%, while in August and September, category C2 prevailed over all stations with 45% (all stations) and 20%, respectively. The categories D1 and D2 were observed at all stations in December and November, with 26% and 31% of events, respectively. These results indicate an increasing trend in the categories B, C1, C2, and D1. © 2023, The Author(s).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85179130577,

title = {Towards understanding climate change: Impact of land use indices and drainage on land surface temperature for valley drainage and non-drainage areas},

author = { S.A. Abu El-Magd and A.M. Masoud and H.S. Hassan and N.M. Nguyen and Q.B. Pham and N.H. Haneklaus and M.W. Hlawitschka and A. Maged},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179130577&doi=10.1016%2fj.jenvman.2023.119636&partnerID=40&md5=034a2c4374bb9fadb2be88bb853a6057},

doi = {10.1016/j.jenvman.2023.119636},

issn = {03014797},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Environmental Management},

volume = {350},

publisher = {Academic Press},

abstract = {The continuous increase of urbanization and industrialization brought various climatic changes, leading to global warming. The unavailability of meteorological data makes remotely sensed data important for understanding climate change. Therefore, the land surface temperature (LST) is critical in understanding global climate changes and related hydrological processes. The main objective of this work is to explore the dominant drivers of land use and hydrologic indices for LST in drainage and non-drainage areas. Specifically, the relationship between LST changes, land use, and hydrologic indices in Northeast Qena, Egypt, was investigated. The Landsat 5 and 8 imagery, Geographic Information System (GIS), and R-package were applied to identify the change detection during 2000–2021. The normalized difference between vegetation index (NDVI), bare soil index (BSI), normalized difference built-up, built-up index (BUI), modified normalized difference water index (MNDWI), and soil-adjusted vegetation index (SAVI) were employed. The non-drainage or mountain areas were found to be more susceptible to high LST values. The comprehensive analysis and assessment of the spatiotemporal changes of LST indicated that land use and hydrologic indices were driving factors for LST changes. Considerably, LST retrieved from the Landsat imaginary showed significant variation between the maximum LST during 2000 (44.82°C) and 2021 (50.74°C). However, NDBI has got less spread during the past (2000) with 10–13%. A high negative correlation was observed between the LST and NDVI, while the SAVI and LST positively correlated. The results of this study provide relevant information for environmental planning to local management authorities. © 2023 Elsevier Ltd},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85204570130,

title = {Three-dimensional evaluation framework of hazard–exposure–vulnerability for mapping heatwave risk and associated dominant dimensions in China},

author = { Jin. Liu and T. Zhang and Y. Ren and P. Willems and A.S. Mirchi and A. Arshad and T. Liu and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204570130&doi=10.1002%2fjoc.8628&partnerID=40&md5=c8d64bf7004ac79c919a25563c6bc1d4},

doi = {10.1002/joc.8628},

issn = {08998418},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Climatology},

publisher = {John Wiley and Sons Ltd},

abstract = {In the context of global warming, the frequency, intensity, and duration of heatwave events have markedly increased, bearing profound implications for both natural ecosystems and human societies. To effectively cope with this challenge, it is imperative to accurately identify and comprehensively assess the risks posed by heatwaves. This study undertakes a systematic approach and robust methodology to assess heatwave risks by leveraging a diverse array of data sources—encompassing remote sensing, statistical analyses. The methodology integrates the risk triangle theory alongside established risk assessment frameworks laid out by the Intergovernmental Panel on Climate Change (IPCC). Employing a three-dimensional evaluation framework encompassing hazard, exposure, and vulnerability, we unravel spatial–temporal patterns, high-risk zones, and dominant dimensions of heatwave risks contributing to potential disasters. Results indicated that during 1999–2008, roughly 27% of the study areas were affected by high and above risk levels of heatwaves, and the areas with high and very high hazard, exposure, and vulnerability accounted for approximately 19.5%, 10%, and 32.5%, respectively. During 2009–2018, the proportion of areas with high and very high risk, hazard, and exposure increased to about 31%, 26%, and 14%, respectively, while the percentage of areas with high and very high vulnerability decreased to about 24.43%. Notably, Xinjiang and the western part of Northwestern China are characterized by hazard-dominant conditions, while Southern China's risk profile has shifted from 1999–2008 to 2009–2018 from high hazard and vulnerability conditions to a more complex interaction involving hazard, exposure, and vulnerability. Moreover, Northern China and the northern segment of Southwestern China exhibit simultaneous high-risk rankings across hazard, exposure, and vulnerability dimensions, forming a comprehensive high-risk zone. These findings characterize heatwave risk patterns and offer critical insights for risk management decisions, guiding effective disaster prevention and relief measures to ensure socio-economic stability and public health. © 2024 Royal Meteorological Society.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85201946531,

title = {Correction to: Data-driven landfill suitability mapping in Lagos State using GIS-based multi-criteria decision making (International Journal of Environmental Science and Technology, (2024), 10.1007/s13762-024-05803-5)},

author = { A. Tella and M.R.U. Mustafa and G. Animashaun and N. Adebisi and C.J. Okolie and A.L. Balogun and Q.B. Pham and R.A. Alani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201946531&doi=10.1007%2fs13762-024-05922-z&partnerID=40&md5=0bfe3a7972e01f7bf26549186135118b},

doi = {10.1007/s13762-024-05922-z},

issn = {17351472},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Environmental Science and Technology},

publisher = {Springer Nature},

abstract = {In this article, some errors/omissions were inadvertently introduced in authors’ affiliations during production. M. R. U. Mustafa was mistakenly listed as 'Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China' but should have been 'Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Perak, Malaysia'. G. Animashaun was mistakenly listed as 'Town Planning Department, Lagos State Ministry of Physical Planning and Urban Development, Ikeja, Nigeria' but should have been 'Lagos State Physical Planning Permit Authority, Development Permit Department, Oba Akinjobi Way, 100271, Ikeja, Lagos State, Nigeria'. C. J. Okolie’s second affiliation is ‘Earth and Environmental Sciences Research Group, Centre for Multidisciplinary Research and Innovation, Abuja, Nigeria’. A.‑L. Balogun was mistakenly listed as ‘Professional Services Department (Resources), EsriAustralia, 613 King Street, West Melbourne, VIC 3003, Australia’ but should have been 'Geospatial Science, School of Science, RMIT University, 402 Swanston Street, Melbourne 3000, Australia'. Original article corrected. © The Author(s) 2024.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85201772570,

title = {The Impact of Circulation Types and their Changing Thermal Properties on the Probability of Days with Snowfall and Rainfall in Poland, 1966-2020},

author = { E.B. Łupikasza and Ł. Małarzewski and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201772570&doi=10.14746%2fquageo-2024-0025&partnerID=40&md5=d14ab7e00d42d3ecbe39714b273dee67},

doi = {10.14746/quageo-2024-0025},

issn = {0137477X},

year  = {2024},

date = {2024-01-01},

journal = {Quaestiones Geographicae},

publisher = {Sciendo},

abstract = {The frequency of snowfall and rainfall is expected to change due to the warming climate. However, trends in liquid and solid phases are not linearly related to air temperature trends. This paper discusses the impact of thermal properties of circulation types (CTs) on the trends in snowy and rainy days in Poland in the period 1966-2020. The visual observations from 42 synoptic stations, which constitute the most-reliable information on precipitation type, were used to identify the precipitation phase. In most CTs, the air temperature increased between 1966-1985 and 2001-2020, but at various rates depending on the type of circulation. Positive tendencies in the thermal properties of CTs contributed to decreasing trends in winter snowfall and increasing trends in winter rainfall. The rate of tendencies in the probability of the precipitation phases depended on the average temperature and the intensity of warming, in particular CTs. In winter, both the snowfall and rainfall tendencies were the strongest for those CTs with average air temperatures (ATs) close to the freezing point, particularly when the average had crossed that threshold between the years 1966-1985 and 2001-2020. The most rapid tendencies in winter snowfall and rainfall, and in the spring mixed phase were induced by N and NW air advection under cyclonic conditions, bringing air from the rapidly warming Arctic. No trends in the winter mixed precipitation probability resulted from its various tendencies in particular CTs. The probability of snowfall increased during air advection from the southeastern sector, particularly in winter. © 2024 Ewa B. Łupikasza et al., published by Sciendo.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85197908889,

title = {Data-driven landfill suitability mapping in Lagos State using GIS-based multi-criteria decision making},

author = { A. Tella and M.R. Raza Ul Mustafa and G. Animashaun and N. Adebisi and C.J. Okolie and A.L. Balogun and Q.B. Pham and R.A. Alani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197908889&doi=10.1007%2fs13762-024-05803-5&partnerID=40&md5=accfce6a682b922e7c8835113a1b80e3},

doi = {10.1007/s13762-024-05803-5},

issn = {17351472},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Environmental Science and Technology},

publisher = {Springer Nature},

abstract = {Careful selection of landfill sites is essential because improper dumping of wastes can negatively impact health and degrade the environment. Therefore, this research presents a Geographic Information System based—Fuzzy Analytic Hierarchy Process multicriteria decision-making approach for landfill zonation in Lagos State, Nigeria. Due to the rapid urbanisation leading to urban expansion and conversion of the landfills to built-up areas in Lagos State, the functioning landfills have been reduced. After a comprehensive literature review, this study considers nine factors: slope, elevation, land use and land cover, lithology, soil type, Normalised Difference Vegetation Index, the distances to roads, distance to settlements, and distance to water bodies. From the decision matrix, the distance to water bodies, distance to roads, distance to settlements, and land use and land (LULC) cover were ranked with percentage weights of 22%, 19%, 17% and 11%, respectively. Afterwards, potential landfill sites were mapped and classified into five classes: very low (626.48 km2; 16.66%), low (1277.56 km2; 33.97%), moderate (1227.97 km2; 32.65%), high (500.52 km2; 13.31%), and very high (128.13 km2; 3.41%). The low and moderate suitability classes have the highest areal coverage due to the state's increased population and urbanisation. A large percentage of the high to very high suitability classes are located in Epe, Ikorodu, and Ibeju-Lekki local government areas (LGAs) which have lower urbanisation levels compared to most of the other LGAs. Therefore, governments and stakeholders should explore these areas for siting of landfills. © The Author(s) 2024.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85194374438,

title = {Assessing river water quality for ecological risk in the context of a decaying river in India},

author = { A.H.M.H. Islam and B.C. Das and S. Mahammad and M.M. Hoque and Q.B. Pham and B. Sarkar and A.R.M.T. Islam and S.C. Pal and A. Quesada-Roman and S.K. Mohinuddin and S.D. Barman},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194374438&doi=10.1007%2fs11356-024-33684-1&partnerID=40&md5=991759ec0e01e3ac62ad790a930b8222},

doi = {10.1007/s11356-024-33684-1},

issn = {09441344},

year  = {2024},

date = {2024-01-01},

journal = {Environmental Science and Pollution Research},

publisher = {Springer},

abstract = {The decay of rivers and river water pollution are common problems worldwide. However, many works have been performed on decaying rivers in India, and the status of the water quality is still unknown in Jalangi River. To this end, the present study intends to examine the water quality of the Jalangi River to assess ecological status in both the spatial and seasonal dimensions. To depict the spatiality of ecological risks, 34 water samples were collected from the source to the sink of the Jalangi River with an interval of 10 km while 119 water samples were collected from a secondary source during 2012–2022 to capture the seasonal dynamics. In this work, the seasonality and spatiality of change in the river’s water quality have been explored. This study used the eutrophication index (EI), organic pollution index (OPI), and overall index of pollution (OIP) to assess the ecological risk. The results illustrated that the values of OPI range from 7.17 to 588, and the values of EI exceed the standard of 1, indicating the critical situation of the ecological status of Jalangi River. The value of OIP ranges between 2.67 and 3.91 revealing the slightly polluted condition of the river water. The study signified the ecological status of the river is in a critical situation due to elevated concentrations of biological oxygen demand, chemical oxygen demand, and low concentrations of dissolved oxygen. The present study found that stagnation of water flow in the river, primarily driven by the eastward tilting of the Bengal basin, triggered water pollution and ecological risk. Moreover, anthropogenic interventions in the form of riverbed agriculture and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river. This decay needs substantial attention from the various stakeholders in a participatory manner. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85183611674,

title = {Application of machine learning-based surrogate models for urban flood depth modeling in Ho Chi Minh City, Vietnam [Formula presented]},

author = { T.Q. Dang and B.H. Tran and Q.N. Le and T.D. Dang and A.H. Tanim and Q.B. Pham and V.H. Bui and P. Scussolini and N.T. Phong and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183611674&doi=10.1016%2fj.asoc.2023.111031&partnerID=40&md5=eec47dd2b3aa4b509491f6209a446388},

doi = {10.1016/j.asoc.2023.111031},

issn = {15684946},

year  = {2024},

date = {2024-01-01},

journal = {Applied Soft Computing},

volume = {150},

publisher = {Elsevier Ltd},

abstract = {Rapid flood prediction in coastal urban areas is an important but challenging task. However, multi-driver floods in coastal areas and their non-linearity in physical processes are hard to represent in physics-based numerical models (PBNMs). In this study, we investigated the performance of surrogate machine learning (ML) models and their flood prediction capability. Initially, we utilize the MIKE+ coupled 1D–2D model to simulate coastal urban flooding in one of the severely flood-affected areas of Ho Chi Minh City (HCMC), Vietnam. Then, nine ML models, including AdaBoost (AB), Decision Tree (DT), Gaussian Process (GP), k-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM) are employed to surrogate the PBNM flood prediction performance and engaged to predict flood depths of the study area domain. 806 simulation scenarios of MIKE+ modeling having a spatial grid of 1107 ×1513, grid size = 2 m, extracting 270,000 inundation points to generate input data for nine ML models are used to simulate surface flood depths for the study area. Results show three ML models, GP, RF, and NN, outperform the remaining models, with R2 value of 0.997, 0.996, and 0.995, respectively. Thus, applying ML models can significantly reduce the simulation time by a PBNM, improve accuracy, and potentially be adopted for real-time forecasting and emergency management. © 2023 Elsevier B.V.},

note = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85182400915,

title = {Long-Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges},

author = { A. Sadeqi and M. Irannezhad and S. Bahmani and K.A. Jelodarlu and S.A. Varandili and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182400915&doi=10.1029%2f2023WR035411&partnerID=40&md5=8b8b1affa4e8070239781911811cb454},

doi = {10.1029/2023WR035411},

issn = {00431397},

year  = {2024},

date = {2024-01-01},

journal = {Water Resources Research},

volume = {60},

number = {1},

publisher = {John Wiley and Sons Inc},

abstract = {In Iran, the mountain snow cover generally feeds major rivers and thereby largely provides water resources required for improving human lives and protecting nature. Hence, understanding historical variability and trends in mountainous snowpack water resources in Iran in response to global warming and climate change can play a critical role in the sustainable development of this country. Accordingly, this study investigated long-term (1982–2018) snowpack climatology at 13 hydrometeorological measurement stations scattered throughout the Iranian mountain ranges, with a focus on Elburz, Azerbaijan, Zagros, and Khorasan mountainous regions. The non-parametric Mann-Kendall test was used to detect statistically significant (p < 0.05) trends, the Pettitt test to identify possible abrupt shift years, the Pearson's correlation coefficient to measure relationships among different time series, and the partial correlation to determine the most important climate factor influencing snowpack dynamics The annual snow depth (maximum snow depth) significantly declined throughout Iranian mountain ranges during 1982–2018, with an average rate of 1.0 (3.4) cm decade−1. The annual snow cover days (SCDs) also showed significant decreasing trends, ranging from 3 to 15 days decade−1 during 1982–2018, in 69% of the stations studied. Such considerable reductions in snow depth and cover days were mainly related to the compound effects of substantial increases in temperature, sunshine, and wind speed as well as decreases in precipitation and cloudiness during the SCDs across the Iranian mountain ranges. However, precipitation was the most influential climate factor controlling snow resources throughout both the Elburz and Zagros mountains in Iran. © 2024. The Authors.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85179587805,

title = {Climate change and future challenges to the sustainable management of the Iraqi marshlands},

author = { A.H. Nama and Z. Saati-Santamaría and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179587805&doi=10.1007%2fs10661-023-12168-8&partnerID=40&md5=edcf001bcc263633058f8ae082091f60},

doi = {10.1007/s10661-023-12168-8},

issn = {01676369},

year  = {2024},

date = {2024-01-01},

journal = {Environmental Monitoring and Assessment},

volume = {196},

number = {1},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035. Additionally, a satellite image–based index model was prepared and applied for the period 2009–2020 to obtain the spatiotemporal distribution of the restored marshlands. The results show that the shortage in water resources and insufficient inundation rates prevented the adequate application of the restoration plans. Also, applying the scenarios of distributing the deficit equally over all water demand sectors (S1) and according to the percentage of demand for each sector (S2) shows that the expected deficit in available water for the three marshes by the years 2025 and 2035 will be approximately 25% and 32% for S1 and 9% for S2. Consequently, the considered marshes are expected to lose approximately 20 to 33% of their eligible restoration areas. Accordingly, looking for suitable alternatives to support the water resources of these marshes became a very urgent matter and/or recourse to reduce the areas targeted by inundation and being satisfied with the areas that can be sustainable and maintain the current status of the rest of the regions as an emerging ecosystem characterized by lands that are inundated every few years. Accordingly, steps must be urged to develop plans and programs to maintain the sustainability of these emerging ecosystems within the frameworks of climate change and the conditions of scarcity of water resources and water and air pollution to ensure that they are not lost in the future. © 2023, The Author(s).},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85168436183,

title = {Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco},

author = { M. Mohamed and M. Sadiki and M. Aghad and Q.B. Pham and M. Batchi and J. Alkarkouri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168436183&doi=10.1080%2f02723646.2023.2250174&partnerID=40&md5=a65ea2310ded3f64123d816004b2a068},

doi = {10.1080/02723646.2023.2250174},

issn = {02723646},

year  = {2024},

date = {2024-01-01},

journal = {Physical Geography},

volume = {45},

number = {2},

pages = {203-230},

publisher = {Taylor and Francis Ltd.},

abstract = {Landslides present a significant hazard to human life, infrastructure, and property, particularly in mountainous regions. In Morocco, these risks have garnered increased attention due to their detrimental impact. This study seeks to model landslide susceptibility using three machine learning classifiers (MLCs): Multi-Layer Perceptron (MLP), Random Forest (RF), and Adaptive Boosting Classifier (AdaBoost), and compare their performance. Initially, 144 landslide sites were identified, and thirteen factors pertaining to landslides were considered. The models’ performance was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). The findings reveal that AUC values range from 68.7% for AdaBoost to 82.2% for RF. The generated landslide susceptibility maps can aid decision-makers in avoiding areas with a high susceptibility to landslides. © 2023 Informa UK Limited, trading as Taylor & Francis Group.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85178244031,

title = {Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm},

author = { A. Masood and M.M. Hameed and A. Srivastava and Q.B. Pham and K. Ahmad and S.F. Mohd Razali and S.A. Baowidan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178244031&doi=10.1038%2fs41598-023-47492-z&partnerID=40&md5=388a8d3f95e7ce7d10bb0417ccb29297},

doi = {10.1038/s41598-023-47492-z},

issn = {20452322},

year  = {2023},

date = {2023-01-01},

journal = {Scientific Reports},

volume = {13},

number = {1},

publisher = {Nature Research},

abstract = {Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R 2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment. © 2023, The Author(s).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85177762073,

title = {Classification of precipitation types in Poland using machine learning and threshold temperature methods},

author = { Q.B. Pham and E.B. Łupikasza and M. Łukasz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177762073&doi=10.1038%2fs41598-023-48108-2&partnerID=40&md5=49a0c8d809885b855aecc51d029b2214},

doi = {10.1038/s41598-023-48108-2},

issn = {20452322},

year  = {2023},

date = {2023-01-01},

journal = {Scientific Reports},

volume = {13},

number = {1},

publisher = {Nature Research},

abstract = {The phase in which precipitation falls—rainfall, snowfall, or sleet—has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966–2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e.; Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station. © 2023, The Author(s).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85174061140,

title = {Understanding the role of training sample size in the uncertainty of high-resolution LULC mapping using random forest},

author = { K. Phinzi and N.S. Ngetar and Q.B. Pham and G.G. Chakilu and S. Szabo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174061140&doi=10.1007%2fs12145-023-01117-1&partnerID=40&md5=9d97f83f38878a2d9e1d01312c00a2f8},

doi = {10.1007/s12145-023-01117-1},

issn = {18650473},

year  = {2023},

date = {2023-01-01},

journal = {Earth Science Informatics},

volume = {16},

number = {4},

pages = {3667-3677},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {High-resolution sensors onboard satellites are generally reputed for rapidly producing land-use/land-cover (LULC) maps with improved spatial detail. However, such maps are subject to uncertainties due to several factors, including the training sample size. We investigated the effects of different training sample sizes (from 1000 to 12;000 pixels) on LULC classification accuracy using the random forest (RF) classifier. Then, we analyzed classification uncertainties by determining the median and the interquartile range (IQR) of the overall accuracy (OA) values through repeated k-fold cross-validation. Results showed that increasing training pixels significantly improved OA while minimizing model uncertainty. Specifically, larger training samples, ranging from 9000 to 12,000 pixels, exhibited narrower IQRs than smaller samples (1000–2000 pixels). Furthermore, there was a significant variation (Chi2 = 85.073; df = 11; p < 0.001) and a significant trend (J-T = 4641; p < 0.001) in OA values across various training sample sizes. Although larger training samples generally yielded high accuracies, this trend was not always consistent, as the lowest accuracy did not necessarily correspond to the smallest training sample. Nevertheless, models using 9000–11,000 pixels were effective (OA > 96%) and provided an accurate visual representation of LULC. Our findings emphasize the importance of selecting an appropriate training sample size to reduce uncertainties in high-resolution LULC classification. © 2023, The Author(s).},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85178127001,

title = {Analyzing Temporal Patterns of Temperature, Precipitation, and Drought Incidents: A Comprehensive Study of Environmental Trends in the Upper Draa Basin, Morocco},

author = { F. El Qorchi and M. Yacoubi-Khebiza and O.A. Omondi and A. Karmaoui and Q.B. Pham and S. Acharki},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178127001&doi=10.3390%2fw15223906&partnerID=40&md5=f8b320da45a237c22693de9ed33446cd},

doi = {10.3390/w15223906},

issn = {20734441},

year  = {2023},

date = {2023-01-01},

journal = {Water (Switzerland)},

volume = {15},

number = {22},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {Quantifying variation in precipitation and drought in the context of a changing climate is important to assess climate-induced changes and propose feasible mitigation strategies, particularly in agrarian economies. This study investigates the main characteristics and historical drought trend for the period 1980–2016 using the Standard Precipitation Index (SPI), Standard Precipitation Evaporation Index (SPEI), Run Theory and Mann–Kendall Trend Test at seven stations across the Upper Draa Basin. The results indicate that rainfall has the largest magnitude over the M’semrir and Agouim (>218 mm/pa) and the lowest in the Agouilal, Mansour Eddahbi Dam, and Assaka subregions (104 mm–134 mm/pa). The annual rainfall exhibited high variability with a coefficient of variation between 35−57% and was positively related to altitude with a correlation coefficient of 0.86. However, no significant annual rainfall trend was detected for all stations. The drought analysis results showed severe drought in 1981–1984, 2000–2001, and 2013–2014, with 2001 being the driest year during the study period and over 75% of both SPEI and SPI values returned drought. Conversely, wet years were experienced in 1988–1990 and 2007–2010, with 1989 being the wettest year. The drought frequency was low (<19%) across all the timescales considered for both SPI and SPEI, with Mansour Eddahbi Dam and Assaka recording the highest frequencies for SPI-3 and SPEI-3, respectively. © 2023 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85174612040,

title = {Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area},

author = { S. Acharki and P.L. Frison and B.K. Veettil and Q.B. Pham and S.K. Singh and M. Amharref and A.S. Bernoussi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174612040&doi=10.1007%2fs10661-023-11877-4&partnerID=40&md5=2dbd734f6ff7873be9d1d2e6558f84d4},

doi = {10.1007/s10661-023-11877-4},

issn = {01676369},

year  = {2023},

date = {2023-01-01},

journal = {Environmental Monitoring and Assessment},

volume = {195},

number = {11},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1); Sentinel-2-MSI (S2); RapidEye (RE); and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85175056560,

title = {Detection and Assessment of Changing Drought Events in China in the Context of Climate Change Based on the Intensity–Area–Duration Algorithm},

author = { Y. Ren and Jin. Liu and P. Willems and T. Liu and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175056560&doi=10.3390%2fland12101820&partnerID=40&md5=f64d56738ddd5b8fa4b1d5b51b564ac3},

doi = {10.3390/land12101820},

issn = {2073445X},

year  = {2023},

date = {2023-01-01},

journal = {Land},

volume = {12},

number = {10},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {Drought can have a significant impact on both society and the economy, resulting in issues such as scarcity of water and shortages of food and energy, as well as elevated health risks. However, as global temperatures continue to rise, the impact of drought events is increasingly exacerbated, manifested by an increase in the frequency, intensity, duration, and spatial extent of their effects. Therefore, studying the changing characteristics of drought events with the background of climate change is of great significance. Based on the high-precision and high-resolution CN05.1 dataset, this study obtained the monthly Standardized Precipitation Evapotranspiration Index (SPEI) dataset from 1961 to 2020, and then identified regional drought events in China using the Intensity–Area–Duration (IAD) method, which considers both temporal continuity and spatial dynamics. On this basis, the spatiotemporal variations in frequency, intensity, duration, and affected area of drought events in China and its seven subregions were analyzed. The results showed that the subregions located in the northern region of China generally have lower mean, maximum, and minimum temperatures than those located in the southern region, but the associated interannual change rate of the subregions in the north is higher than that in the south. As for the annual total precipitation, results show a clear pattern of decreasing southeast–northwest gradient, with an increasing trend in the northern subregions and a decreasing trend in the southern subregions except for the subregion south China (SC). The northeast of China (NE), SC, the southwest of China (SW) and north China (NC) are the regions with a high frequency of drought events in China, while the frequency of drought events in NW and Qinghai–Tibetan Plateau (QTP), although lower, is on a significantly increasing trend, and the increasing rate is higher than for the other regions. For drought intensity, Xinjiang (XJ) and QTP had greater drought intensity, and the change rate of these regions with greater drought intensity was also greater. The drought impact area in China showed a significant increasing trend, mainly concentrated in QTP, NW and NE. Particular attention needs to be focused on the southwest of QTP, where drought events in this region show a significant increase in frequency, intensity, duration and impact area. © 2023 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85169068930,

title = {Could climate change exacerbate droughts in Bangladesh in the future?},

author = { M.F.A. Rahman and M.S.H. Tumon and M.M. Islam and N. Chen and Q.B. Pham and K. Ullah and S.J. Ahammed and S.N. Liza and M.A. Aziz and S. Chakma and M.E. Enan and M.A. Hossain and T. Shufeng and A.M. Dewan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169068930&doi=10.1016%2fj.jhydrol.2023.130096&partnerID=40&md5=524fe89f54d38089af32d0f9f2a05d7d},

doi = {10.1016/j.jhydrol.2023.130096},

issn = {00221694},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Hydrology},

volume = {625},

publisher = {Elsevier B.V.},

abstract = {Droughts are one of the most complex, common, and catastrophic natural disasters, causing severe damage to agriculture and the economy. However, drought susceptibility must be measured and predicted in a systematic way, especially in light of potential climate change scenarios. This study aimed to predict current and future drought susceptibility in Bangladesh using historical climate data (1991–2020) and coupled model intercomparison project 6 data for three seasons: pre-monsoon, monsoon, and post-monsoon. We applied an advanced machine-learning algorithm of artificial neural network (ANN) with a genetic algorithm (GA) optimizer to predict drought-prone areas. Nine hydrological parameters–rainfall, temperature, humidity, cloud coverage, wind speed, sunshine, potential evapotranspiration, and solar radiation–were used to develop drought susceptibility maps. Receiver operating characteristic curves and statistical metrics were used to validate the models. The results of a multilayer perceptron ANN coupled with a GA-based optimizer showed that the relevant statistical measures for training and testing datasets were the root mean square error (RMSE = 0.127 and 0.160) and coefficient of determination (R2 = 0.967 and 0.949) for the pre-monsoon season, monsoon season (RMSE = 0.023 and 0.035; R2 = 0.998 and 0.997), and post-monsoon season (RMSE = 0.083 and 0.142; R2 = 0.986 and 0.959), respectively. Further, drought-prone areas in the baseline drought period of 2020 for pre-monsoon season represented 23.86%, 14.24%, 12.85%, 29.92%, and 19.13% of the total area, respectively; similarly, for monsoon corresponding values were 1.83%, 44.18%, 4.99%, 8.76%, and 40.24%; and for post-monsoon drought they were 24.43%, 20.94%, 16.04%, 37.79%, and 0.80% of the total landmass of Bangladesh. These results can help reduce future drought impacts and be of value in assisting policy responses in the country. © 2023 Elsevier B.V.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85143296465,

title = {Flood susceptibility mapping using qualitative and statistical methods in a semi-arid basin: case of the Manouba–Sijoumi watershed, Northeastern Tunisia},

author = { N. Khadraoui and N. Dahri and Ali Bouamrane and Q.B. Pham and H. Abida},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143296465&doi=10.1007%2fs11600-022-00966-6&partnerID=40&md5=37ad2cc94a33605a0779d266e2d0fd7d},

doi = {10.1007/s11600-022-00966-6},

issn = {18956572},

year  = {2023},

date = {2023-01-01},

journal = {Acta Geophysica},

volume = {71},

number = {5},

pages = {2307-2323},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Floods are considered among the gravest natural disasters worldwide and have resulted in enormous human and material damage. The Manouba–Sijoumi basin (Northeast of Tunisia) is often flooded due to urban expansion, population growth and unplanned land use. This study aims to identify and to define the flood-prone areas of this basin for the 2003 and 2018 extreme events based on a Geographic Information System, a qualitative method (analytic network process-ANP) and a statistical model (frequency ratio-FR). The flood risk maps obtained by both models were validated using the receiver operating characteristic, the area under the curve (AUC) and inventory map. Areas of high and very high flood sensitivity are located mainly in urban settings, with an increase in risk between 2003 and 2018. The AUC values for both models were of the same significance (98%) for the year 2003 while those for the year 2018 were 94% and 98% for the ANP and FR models, respectively. This would imply that both models yielded reasonable results. However, the FR model showed an ability to reduce the uncertainty associated with expert judgements. The results indicate that the most influential factor on flooding in this area was land use/cover. Indeed, populations were largely settled in unsuitable sites for urbanization and in potentially flood-prone areas located mainly around the Sijoumi Sebkha, especially to the west and south of it. The findings of the study are of great value for policy makers and state authorities to achieve greater awareness and adopt strategies for environmental preparedness and management. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85168700534,

title = {Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco},

author = { M. Mohamed and M. Sadiki and Q.B. Pham and A. Zouagui and M. Batchi and J. Alkarkouri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168700534&doi=10.1007%2fs10661-023-11680-1&partnerID=40&md5=d53cb8a680a05b93eb8969390f7abeae},

doi = {10.1007/s10661-023-11680-1},

issn = {01676369},

year  = {2023},

date = {2023-01-01},

journal = {Environmental Monitoring and Assessment},

volume = {195},

number = {9},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q. ilex were identified, and at each site, measurements were taken for a set of 12 geo-environmental parameters including slope, aspect, elevation, geology, distance to stream, rainfall, slope length, plan curvature, profile curvature, erodibility, soil erosion, and land use/land cover. Subsequently, six machine learning algorithms were applied to model optimal areas for reforestation. In terms of models’ performance, the results were compared, and the best were obtained by Bagging (area under the curve (AUC) = 0.98) and Naive Bayes (AUC = 0.97). Extremely favorable areas represent 8% and 17% of the study area according to Bagging and NB respectively, located to the west where geological unit of Bathonian-Bajocian with low erodibility index (K) and where rainfall varies between 250 and 300 mm/year. This work provides a roadmap for decision-makers to increase the chances of successful reforestation at lower cost and in less time. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85166652818,

title = {Mass movement susceptibility prediction and infrastructural risk assessment (IRA) using GIS-based Meta classification algorithms},

author = { S.A. Ali and M. Mohajane and F. Parvin and A. Varasano and S. Hitouri and E.B. Łupikasza and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166652818&doi=10.1016%2fj.asoc.2023.110591&partnerID=40&md5=58888d318882f849f31265878fcfc057},

doi = {10.1016/j.asoc.2023.110591},

issn = {15684946},

year  = {2023},

date = {2023-01-01},

journal = {Applied Soft Computing},

volume = {145},

publisher = {Elsevier Ltd},

abstract = {In mountainous areas, mass movements are among the most dangerous natural hazards. Infrastructure is a crucial component and is thought of as human wealth. This infrastructure is frequently impacted by mass movements, whose frequency and size are anticipated to rise in the future due to the unequal distribution of rainfall events brought on by climate change. To deal with the anticipated repercussions, the study area, the Northern part of Morocco, needs to implement new management and maintenance practices. Thus, the main motivation of this study was to examine the mass movement vulnerability and assess risk on infrastructures in two provinces of North Morocco, i.e. Chefchaouen and Tetouan. The present study employed Reduced Error Pruning Tree (REPTree) and its ensemble with Bagging, AdaBoost, and Random SubSpace (i.e. REPTreeBagging; REPTreeAdaBoost; and REPTreeRandomSubSpace) for mass movement susceptibility mapping (MMSM) based on a comprehensive dataset of 100 mass movements locations which include debris flow, landslide, and rock fall during past 20 years (2000–2020) as well as 12 MM conditioning factors. The result revealed that REPTreeRandomSubSpace is the most viable model for MMSM with AUC = 0.8656. In addition, REPTreeBagging, REPTreeAdaBoost, and REPTree models also offer acceptable results with AUC of 0.8338, 0.8269, and 0.7942, respectively. After MMSM, the infrastructural risk was assessed and the result showed that among the six infrastructural features considered, buildings and forests have a greater risk of mass movement in the study area. Most of the mass movement and infrastructural risk-prone areas are found in the central and north-eastern parts of the study area. The results further revealed that elevation, land use, lithology, rainfall, and distance from roads are important variables for mass movement and infrastructure risk assessment (IRA). The results of this study offer a systematic sight for decision-makers to mitigate natural disasters and infrastructural risk in the study region. © 2023 Elsevier B.V.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85169111137,

title = {Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset},

author = { S. Saravanan and N.M. Reddy and Q.B. Pham and A. Alodah and H.G. Abdo and H. Almohamad and A.A. Al-Dughairi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169111137&doi=10.3390%2fsu151612295&partnerID=40&md5=e3411c6666d84422b21bfa7fd4b49cd0},

doi = {10.3390/su151612295},

issn = {20711050},

year  = {2023},

date = {2023-01-01},

journal = {Sustainability (Switzerland)},

volume = {15},

number = {16},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR); random forest (RF); M5-pruned model (M5P); multilayer perceptron (MLP); and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD); EC-Earth3; EC-Earth3-Veg; MIROC6; MRI-ESM2-0; and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making. © 2023 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85163431904,

title = {Examining the role of class imbalance handling strategies in predicting earthquake-induced landslide-prone regions},

author = { Q.B. Pham and Ö. Ekmekcioğlu and S.A. Ali and K. Koc and F. Parvin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163431904&doi=10.1016%2fj.asoc.2023.110429&partnerID=40&md5=bea3f7fadd08962d9d3f64eaf94e4696},

doi = {10.1016/j.asoc.2023.110429},

issn = {15684946},

year  = {2023},

date = {2023-01-01},

journal = {Applied Soft Computing},

volume = {143},

publisher = {Elsevier Ltd},

abstract = {This study was undertaken to propose a comprehensive prediction scheme containing the hybrid use of class imbalance handling strategies and machine learning methods to assess the earthquake-induced landslide susceptibility for the North Sikkim region. It is worth to mention that taking the class imbalance handling techniques into account is essential to mimic real-world conditions. To tackle this issue, this research for the first time focused on the comprehensive evaluation of nine scenarios comprising four oversampling, four undersampling, and a RAW data analysis techniques. The predictions were conducted with the stochastic gradient boosting (SGB) algorithm. Analysis results depicted that the SVM-SMOTE-SGB outperformed its counterparts (with an AUROC of 0.9878), followed by the models subjected to the pre-processing with BL-SMOTE (AUROC: 0.9876) and RUS (AUROC: 0.9859), respectively. Also, the major drawback of the black-box models, i.e., lack of interpretability, was overcome with a game-theoretical SHapley Additive explanation (SHAP) analysis. The SHAP application with respect to the best-performed model ensured the importance of distance to road, distance to stream, and elevation in the identification of earthquake-induced landslide prone regions. © 2023 Elsevier B.V.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85163041545,

title = {Analysis of daily rainfall concentration in northeastern Algeria 1980–2012},

author = { R. Azioune and A. Benhamrouche and H. Tatar and J. Martin-Vide and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163041545&doi=10.1007%2fs00704-023-04526-w&partnerID=40&md5=43b1788a834aecc5ac46841fd3638fae},

doi = {10.1007/s00704-023-04526-w},

issn = {0177798X},

year  = {2023},

date = {2023-01-01},

journal = {Theoretical and Applied Climatology},

volume = {153},

number = {3-4},

pages = {1361-1370},

publisher = {Springer},

abstract = {This article analyzes the spatial distribution of the values of six daily precipitation concentration indexes in the northeastern part of Algeria. The used indexes were the concentration index (CI) and the Gini index (GI) with the resolution of precipitation amounts 1, 5, and 10 mm. The values of the six indexes are calculated for the period 1980–2012 with 22 stations, and on the other hand, the correlations of the CI1 concentration index with geographical and rainfall variables are analyzed. The values of CI1, CI5, and CI10 and IG1, IG5, and IG10 have been mapped using the tools of the ArcGis10.02 programs. The CI1 concentration index correlates significantly with all the geographic and rainfall variables considered. A negative linear correlation was observed with a linear correlation coefficient r = − 0.67 (p value = 0.0006) between CI1 and altitude. The correlation between CI1 and the distance to sea with a linear correlation coefficient r = − 0.47 (p value = 0.026), the correlation between CI1 and latitude is positive with r = + 0.40 (p value = 0.032). On the other hand, CI1 is significantly positively correlated with average annual precipitation, the coefficient of variation, and the number of rainy days in the same period: r = 0.52 (p value = 0.023); r = − 0.25 (p value = 0.049) and r = 0.49 (p value = 0.022), respectively. © 2023, The Author(s).},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85161703790,

title = {Adapting to Climate-Change-Induced Drought Stress to Improve Water Management in Southeast Vietnam},

author = { P. Thanh and T. Le van and T.T. Minh and T.H. Ngoc and W. Lohpaisankrit and Q.B. Pham and A.S. Gagnon and P. Deb and N.T. Pham and D. Tran Anh and V.N. Dinh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161703790&doi=10.3390%2fsu15119021&partnerID=40&md5=d477ecaa58e95a4ddc40ba60f91a9804},

doi = {10.3390/su15119021},

issn = {20711050},

year  = {2023},

date = {2023-01-01},

journal = {Sustainability (Switzerland)},

volume = {15},

number = {11},

publisher = {MDPI},

abstract = {In Southeast Vietnam, droughts have become more frequent, causing significant damage and impacting the region’s socio-economic development. Water shortages frequently affect the industrial and agricultural sectors in the area. This study aims to calculate the water balance and the resilience of existing water resource allocations in the La Nga-Luy River basin based on two scenarios: (1) business-as-usual and (2) following a sustainable development approach. The MIKE NAM and MIKE HYDRO BASIN models were used for rainfall–runoff (R-R) and water balance modeling, respectively, and the Keetch–Byram Drought Index (KBDI) was used to estimate the magnitude of the droughts. The results identified areas within the Nga-Luy River basin where abnormally dry and moderate drought conditions are common, as well as subbasins, i.e., in the southeast and northeast, where severe and extreme droughts often prevail. It was also shown that the water demand for the irrigation of the winter–spring and summer–autumn crop life cycles could be fully met under abnormally dry conditions. This possibility decreases to 85–100% during moderate droughts, however. In contrast, 65% and 45–50% of the water demand for irrigation is met for the winter–spring and summer–autumn crop life cycles, respectively, during severe and extreme droughts. Furthermore, this study demonstrates that the water demand for irrigation could still be met 100% and 75–80% of the time during moderate, and extreme or severe droughts, respectively, through increased water use efficiency. This study could help managers to rationally regulate water in order to meet the agricultural sector’s needs in the region and reduce the damage and costs caused by droughts. © 2023 by the authors.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85138830121,

title = {Comparative study of machine learning methods and GR2M model for monthly runoff prediction},

author = { P. Ditthakit and S. Pinthong and N. Salaeh and J. Weekaew and T. Thanh Tran and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138830121&doi=10.1016%2fj.asej.2022.101941&partnerID=40&md5=d36f18ed745187e0525e01c448515afa},

doi = {10.1016/j.asej.2022.101941},

issn = {20904479},

year  = {2023},

date = {2023-01-01},

journal = {Ain Shams Engineering Journal},

volume = {14},

number = {4},

publisher = {Ain Shams University},

abstract = {Monthly runoff time-series estimation is imperative information for water resources planning and development projects. This article aims to comparatively investigate the applicability of machine learning (ML) methods (i.e.; Random Forest (RF); M5 model tree (M5); Support Vector Regression with polynomial kernel function (SVR-poly); and Support Vector Regression with the radial kernel function (SVR-rbf)) and the GR2M model for simulating the monthly runoff hydrograph. The models experimented at six runoff stations in Thailand's Southern basin. Four performance criteria, including Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (r), Overall Index (OI), and Combined Index (CI), were utilized for model performance comparison. The finding results revealed that in stations with a low correlation coefficient (r) between input and output data sets, ML algorithms showed superior performance to GR2M. In particular, SVR-rbf showed outstanding performance over other methods. It expressed that SVR-rbf could manage the problem of low-quality data and simulate monthly runoff under limited available data. © 2022 THE AUTHORS},

note = {9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85147954351,

title = {Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model},

author = { E.C. Chukwuma and C.C. Okonkwo and O.O.D. Afolabi and Q.B. Pham and D.C. Anizoba and C.D. Okpala},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147954351&doi=10.1007%2fs11356-023-25447-1&partnerID=40&md5=f0d815bd105dc6c368a84386b283011d},

doi = {10.1007/s11356-023-25447-1},

issn = {09441344},

year  = {2023},

date = {2023-01-01},

journal = {Environmental Science and Pollution Research},

volume = {30},

number = {17},

pages = {49856-49874},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria decision-making (MCDM) model integrating Interval Rough Numbers (IRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) was used to investigate the interrelationships between critical hydrogeologic factors (and determine their relative weights) via a novel vulnerability index based on the DRASTIC model. The flexibility of GIS in handling spatial data was employed to delineate thematic map layers of the hydrogeologic factors and to improve the DRASTIC model. The hybrid MCDM model results show that net recharge (a key hydrogeologic factor) had the highest priority with a weight of 0.1986. In contrast, the topography factor had the least priority, with a weight of 0.0497. A case study validated the hybrid model using Anambra State, Nigeria. The resultant vulnerability map shows that 12.98% of the study area falls into a very high vulnerability class, 31.90% falls into a high vulnerability, 23.52% falls into the average vulnerability, 21.75% falls into a low vulnerability, and 9.85% falls into very low vulnerability classes, respectively. In addition, nitrate concentration was used to evaluate the degree of groundwater pollution. Based on observed nitrate concentration, the modified DRASTIC model was validated and compared to the traditional DRASTIC model; interestingly, the spatial model of the modified DRASTIC model performed better. This study is thus critical for environmental monitoring and implementing appropriate management interventions to protect groundwater resources against indiscriminate sources of pollution. © 2023, The Author(s).},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85143281367,

title = {Flood potential mapping by integrating the bivariate statistics, multi-criteria decision-making, and machine learning techniques},

author = { E. Shahiri Tabarestani and S. Hadian and Q.B. Pham and S.A. Ali and D.T. Phung},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143281367&doi=10.1007%2fs00477-022-02342-8&partnerID=40&md5=68eb63106662c4a9fc8b1a58308de695},

doi = {10.1007/s00477-022-02342-8},

issn = {14363240},

year  = {2023},

date = {2023-01-01},

journal = {Stochastic Environmental Research and Risk Assessment},

volume = {37},

number = {4},

pages = {1415-1430},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This research aims to determine the flood potential mapping within Golestan Province in Iran, applying six novel ensemble techniques guided by the multi-criteria decision-making (MCDM), bivariate statistics, and artificial neural network methods. The combinations of Combined Compromise Solution (COCOSO), Multi-Attributive Border Approximation Area Comparison (MABAC), and multilayer perceptron (MLP) with Frequency Ratio (FR), and Weights of Evidence (WOE) were then generated. It is noted that this is the first application of COCOSO method in flood susceptibility assessment and its efficiency had not been evaluated before. In this regard, 10 flood influential criteria namely altitude, slope, aspect, plan curvature, distance from rivers, Topographic Wetness Index (TWI), rainfall, soil type, geology, and land use, 240 flood points, and 240 non-flood points were employed for the modeling process, of which 70% of such data were chosen for training and remaining 30% for validating. The accuracy of proposed methods was tested by the area under the receiver operating characteristic (AUROC) curve. MABAC-WOE obtained the largest predictive precision (0.937), followed by MLP-WOE (0.934), COCOSO-WOE (0.923), MABAC-FR (0.921), MLP-FR (0.919), and COCOSO-FR (0.892), respectively. The high accuracy of all proposed models represents their capability in flood susceptibility assessment and can guide future flood risk management in the study location. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85151147204,

title = {Ground Surface Deformation Analysis Integrating InSAR and GPS Data in the Karstic Terrain of Cheria Basin, Algeria},

author = { L. Hamdi and N. Defaflia and A. Merghadi and C. Fehdi and P.Y. Ali and J. Dou and Q.B. Pham and H.G. Abdo and H. Almohamad and M. Al-Mutiry},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151147204&doi=10.3390%2frs15061486&partnerID=40&md5=b729752222f7855943e093b5c9b12dc9},

doi = {10.3390/rs15061486},

issn = {20724292},

year  = {2023},

date = {2023-01-01},

journal = {Remote Sensing},

volume = {15},

number = {6},

publisher = {MDPI},

abstract = {Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g.; human losses; economic impact; and environmental degradation) that may occur in the future (e.g.; sinkholes) and can be further utilized in perspective for a sinkhole early warning system. © 2023 by the authors.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85149258932,

title = {Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria},

author = { M. Achite and N. Elshaboury and M. Jehanzaib and D.K. Vishwakarma and Q.B. Pham and D. Tran Anh and E. Mohammed Abdelkader and A. Elbeltagi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149258932&doi=10.3390%2fw15040765&partnerID=40&md5=5bf267571854fc592181491e230d5cc8},

doi = {10.3390/w15040765},

issn = {20734441},

year  = {2023},

date = {2023-01-01},

journal = {Water (Switzerland)},

volume = {15},

number = {4},

publisher = {MDPI},

abstract = {Water resources, land and soil degradation, desertification, agricultural productivity, and food security are all adversely influenced by drought. The prediction of meteorological droughts using the standardized precipitation index (SPI) is crucial for water resource management. The modeling results for SPI at 3, 6, 9, and 12 months are based on five types of machine learning: support vector machine (SVM), additive regression, bagging, random subspace, and random forest. After training, testing, and cross-validation at five folds on sub-basin 1, the results concluded that SVM is the most effective model for predicting SPI for different months (3; 6; 9; and 12). Then, SVM, as the best model, was applied on sub-basin 2 for predicting SPI at different timescales and it achieved satisfactory outcomes. Its performance was validated on sub-basin 2 and satisfactory results were achieved. The suggested model performed better than the other models for estimating drought at sub-basins during the testing phase. The suggested model could be used to predict meteorological drought on several timescales, choose remedial measures for research basin, and assist in the management of sustainable water resources. © 2023 by the authors.},

note = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85147861288,

title = {Evaluating Sediment Yield Response to Watershed Management Practices (WMP) by Employing the Concept of Sediment Connectivity},

author = { H. Nazaripouya and M. Sepehri and A. Atapourfard and B. Ghermezcheshme and Celso A.G. Santos and M. Khoshbakht and S.G. Meshram and V.K. Rana and N.T.T. Linh and Q.B. Pham and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147861288&doi=10.3390%2fsu15032346&partnerID=40&md5=90ff2900e8bea3a58f0a62f723482c7b},

doi = {10.3390/su15032346},

issn = {20711050},

year  = {2023},

date = {2023-01-01},

journal = {Sustainability (Switzerland)},

volume = {15},

number = {3},

publisher = {MDPI},

abstract = {Watershed management practices (WMP) are widely used in catchments as a measure to reduce soil erosion and sediment-related problems. We used a paired catchment in the Gonbad region of Hamadan province, Iran, to evaluate sediment yield response to watershed management practices (WMP) by employing the concept of sediment connectivity (SC). To do this, the SC index as a representation of sediment yield was firstly simulated for the control catchment that there is no WMP. In the next step, the SC index was simulated for impacted catchment, including some WMP, i.e., seeding, pit-seeding, and exclosure. After assessing the accuracy of the produced SC maps using filed observations and erosion plots, the SC maps using quantile-quantile plot (Q-Q plot) were compared to achieve the role of WMP in reducing the rate of sediment yield. The Q-Q plot showed that there is a strong similarity between the SC of catchments, it can be concluded that the WMP has no significant impact on the reducing rate of the sediment yield in this study. © 2023 by the authors.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85146394020,

title = {Separation and attribution of impacts of changes in land use and climate on hydrological processes},

author = { F. Polong and K.A.K. Deng and Q.B. Pham and N.T.T. Linh and S.I. Abba and A.N. Ahmed and D. Tran Anh and K.M. Khedher and A. El-Shafie},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146394020&doi=10.1007%2fs00704-022-04351-7&partnerID=40&md5=ed1fbe7abde077732e2aecd3dfdf5f8b},

doi = {10.1007/s00704-022-04351-7},

issn = {0177798X},

year  = {2023},

date = {2023-01-01},

journal = {Theoretical and Applied Climatology},

volume = {151},

number = {3-4},

pages = {1337-1353},

publisher = {Springer},

abstract = {This study aims to assess, compare, and attribute the effects due to separate and combined land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is set up and calibrated for a small contributing sub-basin of the Tana River Basin (TRB) in Kenya. The model is then applied to simulate the hydrological components (i.e.; streamflow (FLOW); evapotranspiration (ET); soil water (SW); and water yield (WYLD)) for different combinations of LULC and climate scenarios. Land use data generated from Land Satellite 5 Thematic Mapper (Landsat 5TM) images for two different periods (1987 and 2011) and satellite-based precipitation data from the African Rainfall Climatology version 2 (ARC2) dataset are utilized as inputs to the SWAT model. The Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), percent bias (PBIAS), and the ratio of root mean square error to the standard deviation (RSR) for daily streamflow were 0.73, 0.76, 3.16%, and 0.51 in calibration period, respectively, and 0.45, 0.54, 12.53%, and 0.79 in validation period, respectively, suggesting that the model performed relatively good. An analysis of the LULC data for the catchment showed that there was an increase in agricultural, grassland, and forested land with a concomitant decrease in woodland and shrubland. Simulation results revealed that change in climate had a more significant effect on the simulated parameters than the change in LULC. It is shown that changes in LULC only had very minor effects in the simulated parameters. The monthly mean FLOW and WYLD decreased by 0.02% and 0.11%, respectively, while ET and SW increased by a monthly mean of 0.2% and 2.2%. Varying the catchment climate and holding the land use constant reduced FLOW, ET, SW, and WYLD by an average monthly mean of 43.2%, 21%, 13%, and 70%, respectively, indicating that climate changes have more significant effects on the catchment hydrological processes than changes in LULC. Thus, it is necessary to evaluate and identify the isolated and combined effects of LULC and climatic changes when assessing impacts on the TRB’s hydrological processes. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85140054024,

title = {Assessment of climate change impacts on glacio-hydrological processes and their variations within critical zone},

author = { M. Shafeeque and Y. Luo and A. Arshad and S. Muhammad and M.S. Ashraf and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140054024&doi=10.1007%2fs11069-022-05661-9&partnerID=40&md5=8e3e727f6c0ca88a4895e92a63858894},

doi = {10.1007/s11069-022-05661-9},

issn = {0921030X},

year  = {2023},

date = {2023-01-01},

journal = {Natural Hazards},

volume = {115},

number = {3},

pages = {2721-2748},

publisher = {Springer Science and Business Media B.V.},

abstract = {The quantitative assessment of glacier changes and freshwater availability under future climate change is inevitable for sustainable water resources management and preventing natural disasters. Limiting the uncertainties in glacio-hydrological modeling and exploring spatiotemporal distributions of runoff and its components along the vertical profile are critical for such investigations. The present study quantifies glacio-hydrological changes using the Spatial Processes in HYdrology (SPHY) model forced by CMIP6 climate data (shared socioeconomic pathways: SSP126; SSP245; and SSP585) in the Upper Indus Basin (UIB) over twenty-first century. The model was calibrated based on in situ glacier changes, snow cover changes, and streamflow records to avoid the risk of equifinality. Variations in vertical distribution of runoff components and their impact on glacio-hydrology were investigated using the critical zone approach. The projected remaining glacier area is 55 ± 10%, 32 ± 17%, and 15 ± 5%, and freshwater availability is reduced by − 12 ± 5%, − 30 ± 7%, and − 36 ± 6% in 2100, compared with 2005–2014, under SSP126, SSP245, and SSP585, respectively. The average changes in snowmelt, glacier melt, baseflow, and rain-runoff contributions to total runoff under SSP245 are projected as 25 ± 15%, − 30 ± 11%, − 20 ± 16%, and 242 ± 71%, respectively. The critical zone (3500 – 5500 masl) contributes 63% of total runoff during the reference period, with a significant reduction (51–81%) in the projected period, indicating a diminishing influence of glacier runoff (with 50% reduction) in future hydrology compared with historical period. In turn, low flows (October–March) are projected to increase (9 − 58%), and high flows (April–September) will likely decrease (− 2 to − 13%). Warming temperature was identified as the dominant driver for the glacier area changes (r = − 0.85*) and total runoff (r = − 0.75*) at 0.05 level of significance. Our findings indicate a rainfall-runoff-dominant hydrological regime in future, highlighting critical freshwater availability conditions and associated socioeconomic risks in terms of agricultural applications and natural disasters. We recommend building infrastructure for water storage and conveyance in the Indus basin to prevent the adverse impacts of future climate change. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85138110115,

title = {Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers},

author = { D. Tran Anh and A.H. Tanim and D.P. Kushwaha and Q.B. Pham and V.H. Bui},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138110115&doi=10.1016%2fj.ijsrc.2022.08.001&partnerID=40&md5=e651ff4916ce9c5ea0656b5e5687bba3},

doi = {10.1016/j.ijsrc.2022.08.001},

issn = {10016279},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Sediment Research},

volume = {38},

number = {1},

pages = {128-140},

publisher = {Elsevier B.V.},

abstract = {The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution (GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory (LSTM) combined with the Discrete Element Method (DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in three-dimensional (3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples. © 2022 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85146817229,

title = {Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin},

author = { R. Noor and A. Arshad and M. Shafeeque and Jin. Liu and A. Baig and S. Ali and A. Maqsood and Q.B. Pham and A. Dilawar and S.N. Khan and D. Tran Anh and A. Elbeltagi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146817229&doi=10.3390%2frs15020318&partnerID=40&md5=0f6da199107d1f82d0b336b230b67aff},

doi = {10.3390/rs15020318},

issn = {20724292},

year  = {2023},

date = {2023-01-01},

journal = {Remote Sensing},

volume = {15},

number = {2},

publisher = {MDPI},

abstract = {Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g.; zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions. © 2023 by the authors.},

note = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85145969642,

title = {Investigating the Pressure Fluctuations of Hydraulic Jump in an Abrupt Expanding Stilling Basin with Roughened Bed},

author = { M.N. Hasani and K. Nekoufar and M. Biklarian and M. Jamshidi and Q.B. Pham and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145969642&doi=10.3390%2fw15010080&partnerID=40&md5=76719f50fca7d208b575660ed3556a56},

doi = {10.3390/w15010080},

issn = {20734441},

year  = {2023},

date = {2023-01-01},

journal = {Water (Switzerland)},

volume = {15},

number = {1},

publisher = {MDPI},

abstract = {Stilling basins with sudden expansions are one of the energy dissipation structures. In the hydraulic jump, pressure fluctuations cause significant damages in stilling basins by cavity formation, erosion, and vibration. Roughness can also lead to changes of the behavior of stream lines and vortices. Despite the large number of works on the topic, the role of roughness in spatial hydraulic jumps is not yet fully understood. Present research aimed to study the influence of rough bed on pressure fluctuations of S-jump in abrupt expanding stilling basin. Experiments were conducted in a 0.8 m width and 12 m length flume. Channel expansions ratios were 0.33, 0.5, 0.67, and 1 within the range of Froude numbers, 2 to 9.5. The results showed that roughness decreases intensity of pressure fluctuations in an abrupt expansion stilling basin. Additionally, in sudden expanding sections, the energy loss increases, and the intensity of pressure fluctuations decrease due to the formation of lateral vortices. The reduction rate of maximum pressure fluctuation was 27%, 46%, and 58% for expansion ratio of 0.67, 0.5, and 0.33, respectively. The results revealed the clear dependence of these variables on the Froude number and the distance to the hydraulic jump toe. The maximum values of extreme pressure fluctuations occur in the range 0.609 < X < 3.385, where X is dimensionless distance from the toe of the hydraulic jump, which makes it highly advisable to reinforce the bed of stilling basins in this range. © 2022 by the authors.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85143207530,

title = {Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling},

author = { D. Tran Anh and D.V. Thanh and H.M. Le and B. Sy and A.H. Tanim and Q.B. Pham and T.D. Dang and P. Scussolini and N.M. Dang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143207530&doi=10.1007%2fs11269-022-03393-w&partnerID=40&md5=69d27bc421a2737924ce013fe4b59cad},

doi = {10.1007/s11269-022-03393-w},

issn = {09204741},

year  = {2023},

date = {2023-01-01},

journal = {Water Resources Management},

volume = {37},

number = {2},

pages = {639-657},

publisher = {Springer Science and Business Media B.V.},

abstract = {Machine learning and deep learning (ML-DL) based models are widely used for rainfall-runoff prediction and they have potential to substitute process-oriented physics based numerical models. However, developing an ML model has also performance uncertainty because of inaccurate choices of hyperparameters and neural networks architectures. Thus, this study aims to search for best optimization algorithms to be used in ML-DL models namely, RMSprop, Adagrad, Adadelta, and Adam optimizers, as well as dropout techniques to be integrated into the Long Short Term Memory (LSTM) model to improve forecasting accuracy of rainfall-runoff modeling. A deep learning LSTMs were developed using 480 model architectures at two hydro-meteorological stations of the Mekong Delta, Vietnam, namely Chau Doc and Can Tho. The model performance is tested with the most ideally suited LSTM optimizers utilizing combinations of four dropout percentages respectively, 0%, 10%, 20%, and 30%. The Adagrad optimizer shows the best model performance in the model testing. Deep learning LSTM models with 10% dropout made the best prediction results while significantly reducing overfitting tendency of the forecasted time series. The findings of this study are valuable for ML-based hydrological models set up by identifying a suitable gradient descent (GD) optimizer and optimal dropout ratio to enhance the performance and forecasting accuracy of the ML model. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85142282978,

title = {Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms},

author = { Q.B. Pham and B. Mohammadi and R. Moazenzadeh and S. Heddam and R. Pillco Zolá and S. Adarsh and V. Gupta and I. Elkhrachy and K.M. Khedher and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142282978&doi=10.1007%2fs13201-022-01815-z&partnerID=40&md5=7cedc4908eff196077dbd957d8a569c9},

doi = {10.1007/s13201-022-01815-z},

issn = {21905487},

year  = {2023},

date = {2023-01-01},

journal = {Applied Water Science},

volume = {13},

number = {1},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were Xt-1;Xt-2;Xt-3;Xt-4;Xt-12) the ANFIS-WOA model attained root mean square error (RMSE ≈ 0.08 m), mean absolute error (MAE ≈ 0.06 m), and coefficient of determination (R2≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake. © 2022, The Author(s).},

note = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85123998584,

title = {Evaluation and comparison of four satellite-based precipitation products over the upper Tana River Basin},

author = { F. Polong and Q.B. Pham and D. Tran Anh and K.U. Rahman and M.N. Shahid and R.S. Alharbi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123998584&doi=10.1007%2fs13762-022-03942-1&partnerID=40&md5=03f64cd77add9b85f4377a21e9b3de94},

doi = {10.1007/s13762-022-03942-1},

issn = {17351472},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Environmental Science and Technology},

volume = {20},

number = {1},

pages = {843-858},

publisher = {Institute for Ionics},

abstract = {In developing countries, hydrological modeling in support of decision-making and planning of hydrological resources is often hindered by the scarcity of in situ hydro-meteorological data and uneven distribution of observation stations. Satellite-based precipitation products (SPPs) can potentially play a role in overcoming the challenge posed by insufficient and inconsistent in situ precipitation measurements. However, their performance in estimating observations needs to be evaluated before their application in hydrological modeling. This paper evaluates and compares four high-resolution SPPs [ARC2 (African Rainfall Climatology version 2); CHIRPS (Climate Hazard Group Infrared Precipitation with Station data); TMPA 3B42v7 (Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 product); and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)] with measurements from rain-gauge stations (2007–2016) over the Upper Tana River Basin (UTRB). Performance of the SPPs is evaluated using continuous [correlation coefficient (R); mean absolute error (MAE); root mean square error (RMSE); Nash–Sutcliffe efficiency coefficient (NSE); and percent bias (PBIAS)] and categorical [POD (probability of detection); FAR (false alarm ratio); CSI (critical success index); and ETS (equitable threat score)] metrics at daily, dekadal, and monthly time steps. The daily, dekadal, and monthly R of ARC2 SPP were 0.8, 0.92, and 0.94, respectively. The R of CHIRPS (TMPA 3B42v7) is 0.53 (0.63), 0.82 (0.83), and 0.93 (0.95). The daily RMAEs for ARC2, CHIRPS, TMPA 3B42v7, and PERSIANN-CDR are 0.73, 1.27, 1.17, and 1.04, while the RRMSEs are 2.94, 4.52, 4.03, and 3.85, respectively. PBIAS indicates that all the SPPs except CHIRPS underestimated precipitation. It is shown that ARC2 and CHIRPS are better in reproducing the occurrence frequency of daily events for the different intensity ranges. Also, ARC2 was the best performing SPP with regard to the ability to detect precipitation events. It is observed that all SPPs were able to detect low-intensity precipitation events but poor in detecting intensity events higher than 25 mm/day. Overall, ARC2 provided the most accurate precipitation estimates, although CHIRPS and TMPA 3B42v7 also showed good performance in reproducing and detecting precipitation. © 2022, Islamic Azad University (IAU).},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85111855389,

title = {Modelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach},

author = { A.D. Tolche and M.A. Gurara and Q.B. Pham and D.T. Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111855389&doi=10.1080%2f10106049.2021.1959656&partnerID=40&md5=86adeb7aea137c3c12c218bb1ef54cdb},

doi = {10.1080/10106049.2021.1959656},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {24},

pages = {7122-7142},

publisher = {Taylor and Francis Ltd.},

abstract = {Land degradation and desertification have recently become a critical problem in Ethiopia. Accordingly, identification of land degradation vulnerable zonation and mapping was conducted in Wabe Shebele River Basin, Ethiopia. Precipitation derived from Global Precipitation Measurement Mission (GMP), the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized difference vegetation index (NDVI) and land surface temperature (LST), topography (slope), and pedological properties (i.e.; soil depth; soil pH; soil texture; and soil drainage) were used in the current study. NDVI has been considered as the most significant parameter followed by the slope, precipitation and temperature. Geospatial techniques and the Analytical Hierarchy Process (AHP) approach were used to model the land degradation vulnerable index. Validation of the results with google earth image shows the applicability of the model in the study. The result is classified into very highly vulnerable (17.06%), highly vulnerable (15.01%), moderately vulnerable (32.72%), slightly vulnerable (16.40%), and very slightly vulnerable (18.81%) to land degradation. Due to the small rate of precipitation which is vulnerable to evaporation by high temperature in the region, the downstream section of the basis is categorized as highly vulnerable to Land Degradation (LD) and vice versa in the upstream section of the basin. Moreover, the validation using the Receiver Operating Characteristic (ROC) curve analysis shows an area under the ROC curve value of 80.92% which approves the prediction accuracy of the AHP method in assessing and modelling LD vulnerability zone in the study area. The study provides a substantial understanding of the effect of land degradation on sustainable land use management and development in the basin. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85144634572,

title = {Possible Factors Driving Groundwater Quality and Its Vulnerability to Land Use, Floods, and Droughts Using Hydrochemical Analysis and GIS Approaches},

author = { S.A. Abu El-Magd and H. Ahmed and Q.B. Pham and N.T.T. Linh and D. Tran Anh and I. Elkhrachy and A.M. Masoud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144634572&doi=10.3390%2fw14244073&partnerID=40&md5=c9e0130a5eef7597592bc93710383c5f},

doi = {10.3390/w14244073},

issn = {20734441},

year  = {2022},

date = {2022-01-01},

journal = {Water (Switzerland)},

volume = {14},

number = {24},

publisher = {MDPI},

abstract = {Land use and climate change always induce significant changes in various parameters of the hydrologic cycle (e.g.; surface runoff; infiltration; evapotranspiration). The Wadi El-Assiuti downstream area in the Eastern Desert of Egypt is one of the most promising areas for development that is suffering from insufficient water availability and inadequate water quality for different purposes. The main goal of this research is to evaluate the changes in groundwater quality, land use, and climate in association with geology and flooding during three periods within the years 1997–2019 in the downstream portion of Wadi El-Assiuti in the Eastern Desert of Egypt, using spatiotemporal variation associated with groundwater hydrochemical analysis and GIS techniques. About 133 groundwater samples were collected to examine groundwater quality changes over time. Different groundwater quality indices were calculated, and the results show that TDS levels of groundwater in the study area ranged between 1080–2780 mg/L, 672–4564 mg/L, and 811–6084 mg/L, while SAR levels varied within 6.15–15.34, 1.83–28.87, and 1.43–30.57 for the years 1997, 2007, and 2019, respectively. Both RSBC and SSP values exhibited significantly increasing trends over time. KR values were within 1.36–4.06 in 1997, 0.58–14.09 in 2007, and 0.35–14.92 in 2019; MAR values were within 6.9–45.2 in 1997, 20.79–71.5 in 2007, and 17.71–75.81 in 2019; and PI values were within 60.16–83 in 1997, 45.56–101.03 in 2007, and 42.51–148.88 in 2019. Across the entire study area, ongoing land use changes increased from 1.1% in 1997 to 4.1% in 2019. Findings pointed to the significant contribution of the deep Nubian Sandstone Aquifer to the groundwater aquifer at Wadi El-Assiuti through fractures and deep faults. Given the climatic conditions from 1997–2019, these changes may have affected water quality in shallow aquifers, especially with increasing evaporation. Realizing the spatiotemporal variation of the aquifer recharge system, land use development, and climate change clearly would help in water resource management. This study revealed that flooding events, deep-seated geologic structures, and land use development associated with human activities have the highest impact on groundwater quality. © 2022 by the authors.},

note = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85144624873,

title = {Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow},

author = { J. Weekaew and P. Ditthakit and Q.B. Pham and N. Kittiphattanabawon and N.T.T. Linh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144624873&doi=10.3390%2fw14244029&partnerID=40&md5=1e8b8f082bf3976792326d57d88a40c5},

doi = {10.3390/w14244029},

issn = {20734441},

year  = {2022},

date = {2022-01-01},

journal = {Water (Switzerland)},

volume = {14},

number = {24},

publisher = {MDPI},

abstract = {Effective reservoir operation under the effects of climate change is immensely challenging. The accuracy of reservoir inflow forecasting is one of the essential factors supporting reservoir operations. This study aimed to investigate coupling models of feature selection (FS) and machine learning (ML) algorithms to predict the monthly reservoir inflow. The study was carried out using data from the Huai Nam Sai reservoir in southern Thailand. Eighteen years of monthly recorded data (i.e.; reservoir inflow; reservoir storage; rainfall; and regional climate indices) with up to a 12-month time lag were utilized. Three ML techniques, i.e., multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN)were compared in their capabilities. In addition, two FS techniques, i.e., genetic algorithm (GA) and backward elimination (BE) methods, were studied with four predictable time intervals, consisting of 3, 6, 9, and 12 months in advance. Ten-fold cross-validation was used for model evaluation. Study results revealed that FS methods (i.e.; GA and BE) Could improve the performance of SVR and ANN for predicting monthly reservoir inflow forecasting, but they have no effects on MLR. Different developed forecasting models were suitable for different reservoir inflow forecasting time-step-ahead. BE-ANN provided the best performance for three-time-ahead (T + 3) and nine-time-ahead (T + 9) by giving an OI of 0.9885 and 0.8818, NSE of 0.9546 and 0.9815, RMSE of 1.3155 and 1.2172 MCM/month, MAE of 0.9568 and 0.9644 MCM/month, and r of 0.9796 and 0.9804, respectively. The GA-ANN model showed the highest prediction accuracy for six-time-ahead (T + 6), with an OI of 0.8997, NSE of 0.9407, RMSE of 2.1699 MCM/month, MAE of 1.7549 MCM/month, and r of 0.9759. The ANN model showed the best prediction accuracy for twelve-time-ahead (T + 12), with an OI of 0.9515, NSE of 0.9835, RMSE of 1.1613 MCM/month, MAE of 0.9273 MCM/month, and r of 0.9835. © 2022 by the authors.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85136047172,

title = {Study of the impact of ash fallout from the Icelandic volcano Eyjafjöll (2010) on vegetation using MODIS data},

author = { M. Ifkirne and Q. Beri and A. Schaefer and Q.B. Pham and S. Acharki and A. Farah},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136047172&doi=10.1007%2fs11069-022-05544-z&partnerID=40&md5=f55fa25dc25a367ba86a0fdff16ecc21},

doi = {10.1007/s11069-022-05544-z},

issn = {0921030X},

year  = {2022},

date = {2022-01-01},

journal = {Natural Hazards},

volume = {114},

number = {3},

pages = {3811-3831},

publisher = {Springer Science and Business Media B.V.},

abstract = {Volcanic ash fallout is a recurrent environmental disturbance of flora ash deposits from the Icelandic volcano Eyjafjöll (2010) over large areas are responsible for several impacts on ecological processes, agricultural production, and human health in Western Europe. This study assessed the ash fall effects from the subject volcano on the surrounding flora as well as vegetative recovery at two different sites (Scotland and southern Sweden). For this purpose, we analyzed Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by Plants (FPAR) data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). The cited biophysical variables were most strongly influenced by ash cloud fallout, with the lowest maxima recorded for both sites during the 2010 eruption year. To confirm this impact, a statistical study with climate indicators was performed. The results showed a significant correlation between LAI and precipitation (R2 = 0.63; p-value = 0.0022) at site 1 (Scotland), while a weak non-significant correlation (R2 = 0.2248; p-value > 0.5) was observed at site 2. However, climatic data from both sites showed low correlations (R2 < 50%) with NDVI vegetation indicator. Despite the heavy rainfall and heat recorded during this period, our statistical results show us that ash cloud fallout affects vegetative development. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85142730248,

title = {Remote Sensing Data for Geological Mapping in the Saka Region in Northeast Morocco: An Integrated Approach},

author = { A. Elaaraj and A. Lhachmi and H. Tabyaoui and A. Alitane and A. Varasano and S. Hitouri and Y. El Yousfi and M. Mohajane and N. Essahlaoui and H. Gueddari and Q.B. Pham and F. Mobarik and A. Essahlaoui},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142730248&doi=10.3390%2fsu142215349&partnerID=40&md5=c760ee61aa5d933f258839bce6ba7957},

doi = {10.3390/su142215349},

issn = {20711050},

year  = {2022},

date = {2022-01-01},

journal = {Sustainability (Switzerland)},

volume = {14},

number = {22},

publisher = {MDPI},

abstract = {Together with geological survey data, satellite imagery provides useful information for geological mapping. In this context, the aim of this study is to map geological units of the Saka region, situated in the northeast part of Morocco based on Landsat Oli-8 and ASTER images. Specifically, this study aims to: (1) map the lithological facies of the Saka volcanic zone, (2) discriminate the different minerals using Landsat Oli-8 and ASTER imagery, and (3) validate the results with field observations and geological maps. To do so, in this study we used different techniques to achieve the above objectives including color composition (CC), band ratio (BR), minimum noise fraction (MNF), principal component analysis (PCA), and spectral angle mapper (SAM) classification. The results obtained show good discrimination between the different lithological facies, which is confirmed by the supervised classification of the images and validated by field missions and the geological map with a scale of 1/500,000. The classification results show that the study area is dominated by Basaltic rocks, followed by Trachy andesites then Hawaites. These rocks are encased by quaternary sedimentary rocks and an abundance of Quartz, Feldspar, Pyroxene, and Amphibole minerals. © 2022 by the authors.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85141713255,

title = {Impact of Climate Change and Drought Attributes in Nigeria},

author = { A.T. Ogunrinde and P.G. Oguntunde and A.S. Akinwumiju and J.T. Fasinmirin and D.A. Olasehinde and Q.B. Pham and N.T.T. Linh and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141713255&doi=10.3390%2fatmos13111874&partnerID=40&md5=4110d0c2a978c97ba6e27de30594bede},

doi = {10.3390/atmos13111874},

issn = {20734433},

year  = {2022},

date = {2022-01-01},

journal = {Atmosphere},

volume = {13},

number = {11},

publisher = {MDPI},

abstract = {Data from historical observatories and future simulations were analyzed using the representative concentration pathway (RCP) 8.5 scenario, which covered the period from 1951 to 2100. In order to characterize the drought, three widely used drought indicators were used: the standardized precipitation index (SPI), the reconnaissance drought index (RDI), and the standardized precipitation and evapotranspiration index (SPEI). The ensemble of the seven (7) GCMs that used RCA-4 was able to capture several useful characteristics of Nigeria’s historical climatology. Future climates were forecasted to be wetter than previous periods during the study period based on the output of drought characteristics as determined by SPI. SPEI and RDI predicted drier weather, in contrast. SPEI and RDI’s predictions must have been based on the effect of rising temperatures brought on by global warming as depicted by RCP 8.5, which would then have an impact on the rate of evapotranspiration. According to drought studies using the RCP 8.5 scenario, rising temperatures will probably cause more severe/extreme droughts to occur more frequently. SPEI drought frequency changes in Nigeria often range from 0.75 (2031–2060) to 1.80 (2071–2100) month/year, whereas RDI changes typically range from 0.30 (2031–2060) to 0.60 (2071–2100) month/year. The frequency of drought incidence has recently increased and is now harder to forecast. Since the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) and the Sustainable Development Goals (SDGs) have few more years left to be completed, drastic efforts must be made to create climate-resilient systems that can tackle the effects that climate change may have on the water resources and agricultural sectors. © 2022 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85140715523,

title = {Multi-Criteria GIS-Based Analysis for Mapping Suitable Sites for Onshore Wind Farms in Southeast France},

author = { M. Ifkirne and H. El Bouhi and S. Acharki and Q.B. Pham and A. Farah and N.T.T. Linh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140715523&doi=10.3390%2fland11101839&partnerID=40&md5=82019da7bcadc5eb4aa1b60e50b42df2},

doi = {10.3390/land11101839},

issn = {2073445X},

year  = {2022},

date = {2022-01-01},

journal = {Land},

volume = {11},

number = {10},

publisher = {MDPI},

abstract = {Wind energy is critical to traditional energy sources replacement in France and throughout the world. Wind energy generation in France is quite unevenly spread across the country. Despite its considerable wind potential, the research region is among the least productive. The region is a very complicated location where socio-environmental, technological, and topographical restrictions intersect, which is why energy production planning studies in this area have been delayed. In this research, the methodology used for identifying appropriate sites for future wind farms in this region combines GIS with MCDA approaches such as AHP. Six determining factors are selected: the average wind speed, which has a weight of 38%; the protected areas, which have a relative weight of 26%; the distance to electrical substations and road networks, both of which have a significant influence on relative weights of 13%; and finally, the slope and elevation, which have weights of 5% and 3%, respectively. Only one alternative was investigated (suitable and unsuitable). The spatial database was generated using ArcGIS and QGIS software; the AHP was computed using Excel; and several treatments, such as raster data categorization and weighted overlay, were automated using the Python programming language. The regions identified for wind turbines installation are defined by a total of 962,612 pixels, which cover a total of 651 km2 and represent around 6.98% of the research area. The theoretical wind potential calculation results suggest that for at least one site with an area bigger than 400 ha, the energy output ranges between 182.60 and 280.20 MW. The planned sites appear to be suitable; each site can support an average installed capacity of 45 MW. This energy benefit will fulfill the region’s population’s transportation, heating, and electrical demands. © 2022 by the authors.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85140486428,

title = {Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework},

author = { M. Achite and B. Mohammadi and M. Jehanzaib and N. Elshaboury and Q.B. Pham and Z. Duan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140486428&doi=10.3390%2fatmos13101688&partnerID=40&md5=78d672ce27d788079f5929823180dd83},

doi = {10.3390/atmos13101688},

issn = {20734433},

year  = {2022},

date = {2022-01-01},

journal = {Atmosphere},

volume = {13},

number = {10},

publisher = {MDPI},

abstract = {Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average; minimum; and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms)},

note = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85140483566,

title = {Estimating FAO Blaney-Criddle b-Factor Using Soft Computing Models},

author = { S. Thongkao and P. Ditthakit and S. Pinthong and N. Salaeh and I. Elkhrachy and N.T.T. Linh and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140483566&doi=10.3390%2fatmos13101536&partnerID=40&md5=2560e37aa9596a6a96d2ad7ef74c5649},

doi = {10.3390/atmos13101536},

issn = {20734433},

year  = {2022},

date = {2022-01-01},

journal = {Atmosphere},

volume = {13},

number = {10},

publisher = {MDPI},

abstract = {FAO Blaney-Criddle has been generally an accepted method for estimating reference crop evapotranspiration. In this regard, it is inevitable to estimate the b-factor provided by the Food and Agriculture Organization (FAO) of the United Nations Irrigation and Drainage Paper number 24. In this study, five soft computing methods, namely random forest (RF), M5 model tree (M5), support vector regression with the polynomial function (SVR-poly), support vector regression with radial basis function kernel (SVR-rbf), and random tree (RT), were adapted to estimate the b-factor. And Their performances were also compared. The suitable hyper-parameters for each soft computing method were investigated. Five statistical indices were deployed to evaluate their performance, i.e., the coefficient of determination (r2), the mean absolute relative error (MARE), the maximum absolute relative error (MXARE), the standard deviation of the absolute relative error (DEV), and the number of samples with an error greater than 2% (NE > 2%). Findings reveal that SVR-rbf gave the highest performance among five soft computing models, followed by the M5, RF, SVR-poly, and RT. The M5 also derived a new explicit equation for b estimation. SVR-rbf provided a bit lower efficacy than the radial basis function network but outperformed the regression equations. Models’ Applicability for estimating monthly reference evapotranspiration (ETo) was demonstrated. © 2022 by the authors.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85140053886,

title = {Zoning the suitability of the western Mekong Delta for paddy rice cultivation and aquaculture under current and future environmental conditions},

author = { H. Van and X.L.T. Thi and V.L. Le Thi and T.T. Van and N.T. Pham and N.T.D. Phong and A.S. Gagnon and Q.B. Pham and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140053886&doi=10.1007%2fs10661-022-10180-y&partnerID=40&md5=246c2e928d59f69e9dcafd6cf00d3866},

doi = {10.1007/s10661-022-10180-y},

issn = {01676369},

year  = {2022},

date = {2022-01-01},

journal = {Environmental Monitoring and Assessment},

volume = {194},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Ca Mau and Kien Giang, the two provinces of the Mekong Delta bordering the Gulf of Thailand, are facing major environmental challenges affecting the agriculture and aquaculture sectors upon which many livelihoods in this region depend on. This study maps the suitability of these two provinces for paddy rice cultivation and shrimp farming according to soil characteristics and current and future environmental conditions for variables found to significantly influence the yield of those two sectors, i.e., the level of saltwater intrusion, water availability for rainfed agriculture, and the length of the growing period. Future environmental conditions were simulated using the MIKE 11 hydrodynamic model forced by four hydrodynamic scenarios, each one representing different extents of saltwater intrusion during both the dry and rainy seasons, while also considering the availability of water resources for rainfed agriculture. The suitability zoning was performed using a GIS-based analytic hierarchy process (AHP) approach, resulting in the categorisation of the land according to four suitability levels for each sector. The analysis reveals that paddy rice cultivation will become more suitable to Kien Giang province while shrimp farming will be more suitable to Ca Mau province if the simulated future environmental conditions materialise. A suitability analysis is essential for optimal utilisation of the land. The approach presented in this study will inform the regional economic development master plan and provide guidance to other delta regions experiencing severe environmental changes and wishing to consider potential future climatic and sea level changes, and their associated impacts, in their land use planning. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85135859404,

title = {Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin},

author = { S. Ali and D. Liu and Q. Fu and M.J.M. Cheema and S.C. Pal and A. Arshad and Q.B. Pham and Li. Zhang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135859404&doi=10.1016%2fj.jhydrol.2022.128295&partnerID=40&md5=e6dba5958e05980e2c868af38ed2206d},

doi = {10.1016/j.jhydrol.2022.128295},

issn = {00221694},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Hydrology},

volume = {612},

publisher = {Elsevier B.V.},

abstract = {The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely; NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions. © 2022 Elsevier B.V.},

note = {14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85132755051,

title = {Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment},

author = { R. Bag and I. Mondal and M. Dehbozorgi and S.P. Bank and D. N. Das and J. Bandyopadhyay and Q.B. Pham and A. Al-Quraishi and X.C. Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132755051&doi=10.1016%2fj.jclepro.2022.132428&partnerID=40&md5=02ca5fca1a3db840bd7929f4586f75fb},

doi = {10.1016/j.jclepro.2022.132428},

issn = {09596526},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Cleaner Production},

volume = {364},

publisher = {Elsevier Ltd},

abstract = {Sobha watershed, located in the Puruliya district of West Bengal, India, is experiencing severe soil erosion due to specific geo-environmental settings and unscientific land practices. It poses serious threats to agricultural and natural resource development, resulting in land degradation and desertification. This study attempts to identify soil erosion susceptible zones (SESZ) of the Sobha watershed by utilising remote sensing and GIS data products in different machine learning algorithms i.e., Support Vector Machine (SVM), Classification and Regression Tree (CART), Boosted Regression Tree (BRT), and Random Forest (RF)) considering sixteen soil erosion controlling factors (SECFs). In addition, the efficiency of the chosen machine learning models was evaluated using known soil erosion and non-erosion data. The results showed that elevation, drainage density (DD), and normalised difference vegetation index (NDVI) factors contribute the most to soil erosion. The ROC (receiver operating curve) AUC (area under the curve) is used to compare each model, and it was reveals that the RF model performed and predicted the best among them. However, all the models exhibit an outstanding capacity with AUC >85% (RF = 0.97},

note = {13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85132604478,

title = {An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India},

author = { S.A. Ali and F. Parvin and Q.B. Pham and K.M. Khedher and M. Dehbozorgi and Y.W. Rabby and D. Tran Anh and D.H. Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132604478&doi=10.1007%2fs11069-022-05360-5&partnerID=40&md5=74b736320d45e87b3545d617f06a601e},

doi = {10.1007/s11069-022-05360-5},

issn = {0921030X},

year  = {2022},

date = {2022-01-01},

journal = {Natural Hazards},

volume = {113},

number = {3},

pages = {1601-1633},

publisher = {Springer Science and Business Media B.V.},

abstract = {This study examined landslide susceptibility, an increasingly common problem in mountainous regions across the world as a result of urbanization, deforestation, and various natural processes. The Rangit River watershed in Sikkim Himalaya is one of the most landslide-prone areas in India. The main objective of this study was to produce landslide susceptibility maps of the Rangit River watershed using novel ensembles of random forest tree (RFT) with support vector machine (RFT-SVM), artificial neural network (RFT-ANN), naïve Bayes tree (RFT-NBT), and logistic model tree (RFT-LMT). An inventory of landslides was created using historical landslide data, government and scientific studies, and Google Earth’s high-resolution satellite images. The landslide/non-landslide locations were split 70/30 for training and validating the models, respectively. Eleven landslide conditioning factors were selected based on their predictive ability, determined using the information gain method, and each factor’s importance was derived. A landslide susceptibility index was then estimated by weighted overlay using a model builder in a GIS (Geographic Information System) environment. Based on the area under the curve and statistical metrics, RFT-LMT was identified as the best model. The results showed that approximately 40% of the Rangit River watershed has high to very high susceptibility to landslides. This study’s findings will be useful for policy-makers and land use planners in managing and mitigating future landslides in the study area. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85129927825,

title = {Comparison of analytic network process and artificial neural network models for flash flood susceptibility assessment},

author = { N. Dahri and R. Yousfi and Ali Bouamrane and H. Abida and Q.B. Pham and O. Derdous},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129927825&doi=10.1016%2fj.jafrearsci.2022.104576&partnerID=40&md5=0cb6e24f15980d609b642f79d314ac70},

doi = {10.1016/j.jafrearsci.2022.104576},

issn = {1464343X},

year  = {2022},

date = {2022-01-01},

journal = {Journal of African Earth Sciences},

volume = {193},

publisher = {Elsevier Ltd},

abstract = {Floods are natural risks with devastating consequences for the environment, people and the economy. Gabes Catchment is regularly devastated by floods owing to urban sprawl, population growth, unregulated municipal systems and indiscriminate land use. However, mitigation of flood impacts can be achieved via flood implementation forecasting systems. In this study, Analytical Network Process (ANP) and Artificial Neural Network (ANN) models were integrated into a Geographic Information System (GIS) to identify and classify flood-prone areas. A geographic information database was derived from the existing geological map, digital elevation model (DEM), precipitation and land-use data. The evaluation of different factors can affect the flood analysis. The ranked and normalized indicators were then weighted and classified with an ANP model to establish the training database. The normalized layers, combined with the training site maps, were then fed to a multilayer perceptron neural network (MLP) to yield a flood risk map. Using a field survey, historical flood data, and satellite imagery, 226 flood locations were identified and classified into 70% training data sets and 30% validation data. Results obtained from ANP and ANN showed that 10% and 14% of all areas were classified as high and very high flood susceptibility, respectively. The performance of both models was assessed using the operational characteristic of the ROC model. The Area Under the Curve ‘AUC’ of ANP and ANN models were 0.861 and 0.876, respectively. The obtained results show the similarity and comparability of the used methods. These results corroborate the perception of susceptibility in the population of the city of Gabes. The study outcomes are of great value to policy makers and state authorities in order to achieve greater awareness and adopt strategies for the preparation and management of the environment in the future for the city of Gabes. © 2022 Elsevier Ltd},

note = {12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85127604255,

title = {Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach},

author = { Q.B. Pham and S.A. Ali and E. Bielecka and B. Calka and A. Orych and F. Parvin and E.B. Łupikasza},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127604255&doi=10.1007%2fs11069-022-05336-5&partnerID=40&md5=3f672a3aa225c24656490d62d9f2b9ef},

doi = {10.1007/s11069-022-05336-5},

issn = {0921030X},

year  = {2022},

date = {2022-01-01},

journal = {Natural Hazards},

volume = {113},

number = {2},

pages = {1043-1081},

publisher = {Springer Science and Business Media B.V.},

abstract = {Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings’ exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.},

note = {12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85116744296,

title = {Investigating feasible sites for multi-purpose small dams in Swat District of Khyber Pakhtunkhwa Province, Pakistan: socioeconomic and environmental considerations},

author = { A. Hussain and K.U. Rahman and M.N. Shahid and S. Haider and Q.B. Pham and N.T.T. Linh and S.S. Sammen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116744296&doi=10.1007%2fs10668-021-01886-z&partnerID=40&md5=281eee1d44c47d0c8accd863de4ae98b},

doi = {10.1007/s10668-021-01886-z},

issn = {1387585X},

year  = {2022},

date = {2022-01-01},

journal = {Environment, Development and Sustainability},

volume = {24},

number = {9},

pages = {10852-10875},

publisher = {Springer Science and Business Media B.V.},

abstract = {The low water storage capacity caused water crisis in Pakistan; therefore, the country needs both small- and large-scale reservoirs to store surplus water resources. The construction of large dams in Pakistan could not be materialized due to financial and political constraints. However, the construction of multi-purpose small dams is the next best option to store water. To this end, there is a need to identify the best feasible sites in the country. The selection of feasible sites for multi-purpose dams must take into account multiple criteria, including engineering, socioeconomic and environmental. The current study utilizes the coupled Remote Sensing and Geographical Information System techniques to identify the feasible sites for multi-purpose small dams, considering the socioeconomic and environmental criteria in addition to the established engineering criteria in district Swat, Khyber Pakhtunkhwa. The suitability map considered nine engineering criteria, including rainfall distribution, slope, land use, curve number, runoff depth, soil, alluvial depth, closeness to streams, and drainage density, using the weights calculated from priority indices. The suitability map is divided into four classes, i.e., excellent, good, moderate, and unsuitable with the excellent and good classes area of 66.78 km2, and 195.75 km2. Twenty sites (based on accessibility and closeness to Swat River) from each class are selected that are situated in Kalam, Babozai, Bahrain, Madyan, Khwazakhela, Matta, Kabal, and Barikot areas of district Swat and evaluated using socioeconomic and environmental criteria, i.e., community well and no displacement cost, management ownership–private or public, biodiversity protection services, instrumental in groundwater recharge for the community, electricity generation for the local community, low maintenance cost, flood friendly, appropriate distribution of water resources, political well, and irrigation and drinking water potential. The top priority for these areas is electricity generation, flood protection, irrigation and drinking water capability, sustainable operation, low maintenance cost and political well. The current study demonstrated that socioeconomic and environmental criteria augment the engineering approach in identifying the best feasible site for multi-purpose small dams. These sites would not only store the water but would also provide important services (electricity generation; irrigation water; etc.) to the local community and economy. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85131807019,

title = {Comparison of machine learning and process-based SWAT model in simulating streamflow in the Upper Indus Basin},

author = { K.U. Rahman and Q.B. Pham and K.Z. Jadoon and M.N. Shahid and D.P. Kushwaha and Z. Duan and B. Mohammadi and K.M. Khedher and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131807019&doi=10.1007%2fs13201-022-01692-6&partnerID=40&md5=1e3a27ae19f6e693d81bf9acbad90734},

doi = {10.1007/s13201-022-01692-6},

issn = {21905487},

year  = {2022},

date = {2022-01-01},

journal = {Applied Water Science},

volume = {12},

number = {8},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models. © 2022, The Author(s).},

note = {9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85131039476,

title = {Projection of maximum temperatures to 2050 in the Bourgogne-Franche-Comté region, France},

author = { M. Ifkirne and Z. Duclos and A. Farah and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131039476&doi=10.1007%2fs00704-022-04099-0&partnerID=40&md5=87b92440ae038778194dbd64c6ba1d34},

doi = {10.1007/s00704-022-04099-0},

issn = {0177798X},

year  = {2022},

date = {2022-01-01},

journal = {Theoretical and Applied Climatology},

volume = {149},

number = {3-4},

pages = {1153-1166},

publisher = {Springer},

abstract = {In this study, we will attempt to spatialize the daily maximum temperature projections estimated for 2050 by the ALADIN-CLIMAT model by using three types of spatial interpolation methods (statistical interpolation by regression; deterministic interpolation by inverse distance weighting and geostatistical interpolation by Kriging) and their results. The 3 interpolation methods tested allow us to establish the following synthesis with regard to the summary table of estimated errors. For the (IDW), this method, simple to apply, presents low errors (0.31). However, the visual representation is not optimal with some inconsistencies in the interpolation result. On the other hand, geostatistical interpolation by kriging: in comparison with the IDW and linear regression methods, this method has the lowest errors (0.23), but visually, this is the interpolation map that includes the most inconsistencies. This result is not surprising, since the distribution of the NORTXAV variable does not meet the distribution normality conditions for applying kriging interpolation. However, statistical interpolation by linear regression: compared to the other methods applied, this method has the largest errors. Nevertheless, this model seems very reliable with an R2 of 91%. Moreover, the different parameters have been validated for the application of the model: linear relationship between x and y, absence of multicollinearity, normality/independence, and relative homoscedasticity of the residuals. However, upon visual analysis, some inconsistencies can also be detected, and one may wonder if there is a risk of over-interpreting the data within the mesh. In conclusion, we recommend the linear regression method as it seems to be the most efficient method to interpolate temperature projections. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85139457800,

title = {Long short-term memory (LSTM) neural networks for short-term water level prediction in Mekong river estuaries},

author = { T.T. Tran and N. Liem and P.N. Hoai and Q.B. Pham and P.T.T. Huyen and N. Dong and H.H. Hieu and N.T. Hien},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139457800&partnerID=40&md5=72e39c9e0b536bfe47e366ce092be281},

issn = {01253395},

year  = {2022},

date = {2022-01-01},

journal = {Songklanakarin Journal of Science and Technology},

volume = {44},

number = {4},

pages = {1057-1066},

publisher = {Prince of Songkla University},

abstract = {This study firstly adopts a state-of-the-art deep learning approach based on a Long Short-Term Memory (LSTM) neural network for predicting the hourly water level of Mekong estuaries in Vietnam. The LSTM models were developed from around 8,760 hourly data points within 2018 and were evaluated using the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). The results showed that the NSE values for the training and testing steps were both above 0.98, which can be regarded as very good performance. Furthermore, the RMSE were between 0.09 and 0.11 m for the training and between 0.10 and 0.12 m for the testing, while MAE for the training ranged from 0.07 to 0.08 m and varied from 0.08 to 0.10 m for the testing. The LSTM networks appear to enable high precision and robustness in water level time series prediction. The outcomes of this research have crucial implications in river water level predictions, especially from the viewpoint of employing deep learning algorithms. © 2022, Prince of Songkla University. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85136256133,

title = {Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale},

author = { S. Hitouri and A. Varasano and M. Mohajane and S. Ijlil and N. Essahlaoui and S.A. Ali and A. Essahlaoui and Q.B. Pham and M. Waleed and S.K. Palateerdham and A.C.M. Teodoro},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136256133&doi=10.3390%2fijgi11070401&partnerID=40&md5=f3e6625cf1c4d8aaf0a3ec2539c54747},

doi = {10.3390/ijgi11070401},

issn = {22209964},

year  = {2022},

date = {2022-01-01},

journal = {ISPRS International Journal of Geo-Information},

volume = {11},

number = {7},

publisher = {MDPI},

abstract = {Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e.; elevation; slope; aspect; plane curvature; topographic moisture index (TWI); stream power index (SPI); precipitation; distance to road; distance to stream; drainage density; land use; and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area. © 2022 by the authors.},

note = {15},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85133131122,

title = {Landslide susceptibility modelling using hybrid bivariate statistical-based machine-learning method in a highland segment of Southern Western Ghats, India},

author = { A.L. Achu and C. D; Aju and Q.B. Pham and R. Reghunath and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133131122&doi=10.1007%2fs12665-022-10464-z&partnerID=40&md5=4b214e16eb6fa09f69314a0800966424},

doi = {10.1007/s12665-022-10464-z},

issn = {18666280},

year  = {2022},

date = {2022-01-01},

journal = {Environmental Earth Sciences},

volume = {81},

number = {13},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The unique physiographic conditions, abundant rainfall, and anthropogenic disturbances in the mountainous Western Ghats (WG) region often cause landslides, which leads to loss of life and properties. The present study aims to generate a landslide susceptibility map for a mountainous WG region using novel hybrid methods. For this, a landslide inventory database is prepared with 82 landslide locations and twelve landslide contributing factors, viz., lithology, geomorphology, slope angle, soil texture, relative relief, distance from the streams, distance from the roads, distance from the lineaments, topographic wetness index (TWI), rainfall, land use/land cover, and slope curvature were used for modelling. Initially, four bivariate approaches, viz., information value (InfoV), evidential belief function (EBF), certainty factor (CF), and index of entropy methods (IoE), were applied to model landslide susceptibility, and the accuracy was estimated. Thereafter, these models were integrated with random forest (RF) to build hybrid models. The computed hybrid models were evaluated using ROC–AUC and confusion matrix-based statistical measures. Hybrid models show higher accuracy with ROC–AUC values ranging from 0.894 to 0.928 in the training phase and 0.906 to 0.925 in the testing phase. The RF-InfoV model was proven to be comparably efficient for landslide susceptibility analysis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85130890048,

title = {Region of freshwater influence (ROFI) and its impact on sediment transport in the lower Mekong Delta coastal zone of Vietnam},

author = { N.M. Nguyen and D. San and K.D. Nguyen and Q.B. Pham and A.S. Gagnon and S.T. Mai and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130890048&doi=10.1007%2fs10661-022-10113-9&partnerID=40&md5=8e7e8c499a9f23a7f82dffbee35cc646},

doi = {10.1007/s10661-022-10113-9},

issn = {01676369},

year  = {2022},

date = {2022-01-01},

journal = {Environmental Monitoring and Assessment},

volume = {194},

number = {7},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The delta of the Mekong River is one of the largest in the world, with the Mekong River carrying a large amount of sediments in its Region of Freshwater Influence (ROFI). This study investigates the flow structure and movement of both suspended and bedload sediments in the ROFI of the Lower Mekong Delta (LMD) in order to identify areas prone to sediment accretion and erosion. This is accomplished by applying the three-dimensional Coastal and Regional Ocean COmmunity (CROCO) model and then calculating the sediment budget of different stretches of the coastline. The model outputs, depicting areas experiencing sediment accretion and erosion along the coastline of the LMD, are then compared against observations obtained during the period 1990–2015 and demonstrate the ability of the model to identify areas particularly prone to erosion and where preventive actions against coastal erosion should focus. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85130106481,

title = {Cooling island effect of urban lakes in hot waves under foehn and climate change},

author = { C.L. Le Phuc and H.S. Nguyen and D.D. Cham and N.B. Tran and Q.B. Pham and X.C. Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130106481&doi=10.1007%2fs00704-022-04085-6&partnerID=40&md5=508fbeb00b92c72d48e5684763b91d65},

doi = {10.1007/s00704-022-04085-6},

issn = {0177798X},

year  = {2022},

date = {2022-01-01},

journal = {Theoretical and Applied Climatology},

volume = {149},

number = {1-2},

pages = {817-830},

publisher = {Springer},

abstract = {The central region of Vietnam has a tropical monsoon climate but often undergoes heat waves due to uncontrolled urbanization, foehn winds, and climate change. Water bodies are considered effective candidates for heat mitigation in cities through the water cooling island (WCI) effect. Quantifying the WCI capacity of water areas and related factors is necessary for sites with advantages of surface water. The current attempt used the WCI effect range (Lmax), temperature drop amplitude (ΔTmax), and temperature gradient (Gtemp) to investigate the cooling effect of 20 lakes in the Thanh Noi region, Hue City. Data derived from high-resolution Google Earth, Landsat-8 Satellite Imagery Data, and ground truth. The results show that the average water temperature of the 20 studied lakes was about 36.61 °C, lower than the average temperature in the area with an urban heat island (UHI) of about 2.82 °C. The mean Lmax was 150 m, ΔTmax was 1.52 °C, and Gtemp was 10.16 °C /km or 0.01 °C/m. Climate characteristics and human impacts had reduced the ability of the lakes to create WCI during the period when the lake water level was low. The factors that influenced the WCI significantly were the landscape shape index (LSI), the proportion of green (PG), and the percentage of impervious surfaces (PI). Most lakes with relatively simple LSI, high PG, and low PI obtained high WCI, suggesting that structural and landscape characteristics played a critical role in urban cooling. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85129549555,

title = {Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms},

author = { F. Parvin and S.A. Ali and B. Calka and E. Bielecka and N.T.T. Linh and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129549555&doi=10.1007%2fs00704-022-04068-7&partnerID=40&md5=3f69367a1a43cdc2f07dd05cade624ff},

doi = {10.1007/s00704-022-04068-7},

issn = {0177798X},

year  = {2022},

date = {2022-01-01},

journal = {Theoretical and Applied Climatology},

volume = {149},

number = {1-2},

pages = {639-659},

publisher = {Springer},

abstract = {Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland, using three machine learning models, such as the Bayesian logistic regression (BLR), the artificial neural networks (ANN), and the deep learning neural networks (DLNNs). The perfomance of these three methods was assessed in order to select the best method for flood vulnerability mapping in densely urbanized city. Thus, initially, thirteen flood predictors were evaluated using the information gain ratio (IGR), and eight most important predictors were considered from model training and testing. The performance of the applied models and accuracy of the result was evaluated through the area under the curve (AUC) and statistical measures. By using the testing dataset, the result reveals that DLNN (AUC = 0.877) is the more performant model in comparison to ANN (AUC = 0.851) and BLR (AUC = 0.697). However, the BLR model has the lowest predictive capability. The results of the present study could be effectively used for the urban flood management strategies. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85127391420,

title = {Estimation of Tasuj aquifer response to main meteorological parameter variations under Shared Socioeconomic Pathways scenarios},

author = { B. Ghazi and E. Jeihouni and O. Kisi and Q.B. Pham and B. Ðurin},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127391420&doi=10.1007%2fs00704-022-04025-4&partnerID=40&md5=3119655dea67e135756eb6d6f4e4ab84},

doi = {10.1007/s00704-022-04025-4},

issn = {0177798X},

year  = {2022},

date = {2022-01-01},

journal = {Theoretical and Applied Climatology},

volume = {149},

number = {1-2},

pages = {25-37},

publisher = {Springer},

abstract = {In this study, statistical and soft-computing methods are compared in forecasting groundwater levels under Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) in Tasuj Plain, Iran, for a near future period (2022–2027). A combination of general circulation models (GCMs) was used in the projection of precipitation and temperature in the future period. The estimation of climate variables for 2020–2044 period indicated that the temperature will increase, while the precipitation will decline. In simulation temporal groundwater level, wavelet-nonlinear autoregressive network with exogenous inputs (NARX), autoregressive integrated moving average (ARIMA), and wavelet-adaptive neuro-fuzzy inference system (ANFIS) models were used. The comparison of performance criteria for these models in the simulation of groundwater level demonstrated that the wavelet-NARX model with the R2 of 0.99 has shown better efficiency. Finally, the simulation of groundwater level through the wavelet-NARX model was carried out for different scenarios. The results indicated that future groundwater levels in Tasuj Plain would continue to decline by 3.12 m, 3.96 m, and 4.79 m, for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85126857050,

title = {Evaluation of Satellite Rainfall Estimates in a Rugged Topographical Basin Over South Gojjam Basin, Ethiopia},

author = { D.A. Malede and T. Alamirew and J.R. Kosgei and Q.B. Pham and T.G. Andualem},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126857050&doi=10.1007%2fs12524-022-01530-x&partnerID=40&md5=a02933ae4ae5ce5003e30f3f340e9bbe},

doi = {10.1007/s12524-022-01530-x},

issn = {0255660X},

year  = {2022},

date = {2022-01-01},

journal = {Journal of the Indian Society of Remote Sensing},

volume = {50},

number = {7},

pages = {1333-1346},

publisher = {Springer},

abstract = {Under the inaccessibility of optimum networks and lack of well-organized rain gauge data, the provided high-resolution satellite estimates serve as a vital baseline for hydro climate-associated studies. In the rugged topography of South Gojjam Basin, satellite rainfall estimates from the African Rainfall Climatology (ARC2), Climate Hazard Group Infrared Precipitation with Stations Data (CHIRPSv2), African Rainfall Estimations Algorithm (REFv2), and Tropical Applications of Meteorology using Satellite (TAMSATv3) were evaluated and compared with rain gauge rainfall data. Satellite rainfall estimates were evaluated at spatiotemporal scales with observed data using approaches of point observed rainfall data and areal averaged rainfall comparisons for the period of 2001–2018. The basin was divided into two parts, according to geographical region, to identify the effect of topographical variation. Besides, the spatial map of the annual satellite rainfall estimate pattern was illustrated with observed rainfall data for comparison. The result showed that TAMSATv3 and RFEv2 rainfall estimates showed the best performance than ARC2 and CHIRPSv3 products at daily time scales and in both spatial scales (i.e.; higher; and lower elevations). On a monthly and wet season timescale, the CHIRPSv2 product was outperformed, even though the four satellite products performed better. CHIRPSv2 and TAMSATv3 products presented overestimated rainfall in the lower elevation region at daily and monthly scales, while ARC2 and RFEv2 products were underestimated at all spatiotemporal scales. Overall, CHIRPSv2 and TAMSATv3 satellite rainfall estimates showed good relation to the rain gauge rainfall data in the rugged topography and a limited number of rain gauges in the South Gojjam basin. Thus, this study decided that CHIRPSv2 and TAMSATv3 satellite rainfall data could be used as an alternative to rain gauge rainfall on a monthly and wet season time scale for hydroclimate studies. © 2022, Indian Society of Remote Sensing.},

note = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85125521244,

title = {Groundwater level prediction using machine learning algorithms in a drought-prone area},

author = { Q.B. Pham and M.S. Kumar and F. Di Nunno and A. Elbeltagi and F. Granata and A.R.M.T. Islam and S. Talukdar and X.C. Nguyen and A.N. Ahmed and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125521244&doi=10.1007%2fs00521-022-07009-7&partnerID=40&md5=4eee868d19776c11d068a70cb124a0dd},

doi = {10.1007/s00521-022-07009-7},

issn = {09410643},

year  = {2022},

date = {2022-01-01},

journal = {Neural Computing and Applications},

volume = {34},

number = {13},

pages = {10751-10773},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life, and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in the sustainable management of GWR. A comparative study was conducted to evaluate the performance of seven different ML models, such as random tree (RT), random forest (RF), decision stump, M5P, support vector machine (SVM), locally weighted linear regression (LWLR), and reduce error pruning tree (REP Tree) for GW level (GWL) prediction. The long-term prediction was conducted using historical GWL, mean temperature, rainfall, and relative humidity datasets for the period 1981–2017 obtained from two wells in the northwestern region of Bangladesh. The whole dataset was divided into training (1981–2008) and testing (2008–2017) datasets. The output of the seven proposed models was evaluated using the root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), correlation coefficient (CC), and Taylor diagram. The results revealed that the Bagging-RT and Bagging-RF models outperformed other ML models. The Bagging-RT models can effectively improve prediction precision as compared to other models with RMSE of 0.60 m, MAE of 0.45 m, RAE of 27.47%, RRSE of 30.79%, and CC of 0.96 for Rajshahi and RMSE of 0.26 m, MAE of 0.18 m, RAE of 19.87%, RRSE of 24.17%, and 0.97 for Rangpur during training, and RMSE of 0.60 m, MAE of 0.40 m, RAE of 24.25%, RRSE of 29.99%, and CC of 0.96 for Rajshahi and RMSE of 0.38 m, MAE of 0.24 m, RAE of 23.55%, RRSE of 31.77%, and CC of 0.95 for Rangpur during testing stages, respectively. Our study offers an effective and practical approach to the forecast of GWL that could help to formulate policies for sustainable GWR management. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.},

note = {37},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85130409338,

title = {Performances of Different Machine Learning Algorithms for Predicting Saltwater Intrusion in the Vietnamese Mekong Delta Using Limited Input Data: A Study from Ham Luong River},

author = { T.T. Tran and N.H. Pham and Q.B. Pham and T.L. Pham and X. Ngo and D.L. Nguyen and P.N. Nguyen and B.K. Veettil},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130409338&doi=10.1134%2fS0097807822030198&partnerID=40&md5=10334505b8a1b3669acbfe597cc02320},

doi = {10.1134/S0097807822030198},

issn = {00978078},

year  = {2022},

date = {2022-01-01},

journal = {Water Resources},

volume = {49},

number = {3},

pages = {391-401},

publisher = {Pleiades journals},

abstract = {Abstract: Accurate forecasting of salinity intrusion has a vital role in water resource management to mitigate and prevent its adverse effects. However, monitoring of salinity presents great challenges because the task requires a number of information, such as hydrological, geomorphological data. The objective of this paper is to compare the performances of different machine learning algorithms for saltwater intrusion prediction using a limited number of input data. To achieve this goal, we tested the performances of five algorithms (i.e.; Simple Linear; K-Nearest Neighbors; Random Forest; Support Vector Machine and a deep learning algorithm Long Short Term Memory) for predicting saltwater intrusion in the Ham Luong River in the Vietnamese Mekong Delta using only salinity monitoring data. We used Nash−Sutcliffe efficiency coefficient, Mean Absolute Error, and Root Mean Square Error to evaluate the performances of the above mentioned five models. Our results showed that Long Short Term Memory was the most accurate and efficient model, which implies that deep learning algorithms might be more efficient than machine learning algorithms in case of limited input data. Besides, the study also showed that performance of the linear model was insignificant compared to the non-linear algorithms. The results also revealed that saltwater intrusion forecasts could be achieved even in the limited data context. The present study provided a precise and simple tool for early warning of saltwater intrusion in the Vietnamese Mekong Delta. © 2022, Pleiades Publishing, Ltd.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85129842085,

title = {Observed Changes in Crop Yield Associated with Droughts Propagation via Natural and Human-Disturbed Agro-Ecological Zones of Pakistan},

author = { F. Saleem and A. Arshad and A.S. Mirchi and T. Khaliq and X. Zeng and M.M. Rahman and A. Dilawar and Q.B. Pham and K. Mahmood},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129842085&doi=10.3390%2frs14092152&partnerID=40&md5=8c3c5fcf3dc8df25cb5252a2edd1dfbe},

doi = {10.3390/rs14092152},

issn = {20724292},

year  = {2022},

date = {2022-01-01},

journal = {Remote Sensing},

volume = {14},

number = {9},

publisher = {MDPI},

abstract = {Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-me-teorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e.; ground observations; reanalysis; and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g.; North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g.; Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (<−2) of standardized drought severity index (DSI) observed during the drought episodes (1988; 2000; and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85128754241,

title = {Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations},

author = { M.S. Kumar and A. Elbeltagi and C.B. Pande and A.N. Ahmed and C.M. Fai and Q.B. Pham and A. Kumari and De. Kumar},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128754241&doi=10.1007%2fs11269-022-03136-x&partnerID=40&md5=7aaee30b8ac7be3921a9487f7722a3f2},

doi = {10.1007/s11269-022-03136-x},

issn = {09204741},

year  = {2022},

date = {2022-01-01},

journal = {Water Resources Management},

volume = {36},

number = {7},

pages = {2201-2221},

publisher = {Springer Science and Business Media B.V.},

abstract = {Accurate and reliable discharge estimation is considered vital in managing water resources, agriculture, industry, and flood management on the basin scale. In this study, five data-driven tree-based algorithms: M5-Pruned model-M5P (Model-1), Random Forest-RF (Model-2), Random Tree-RT (Model-3), Reduced Error Pruning Tree-REP Tree (Model-4), and Decision Stump-DS (Model-5) have been examined to measure the daily discharge of Govindpur site at Burhabalang river, India. The proposed models will be calibrated by daily 10-years time-series hydrological data (i.e.; river stage (h) and daily discharge (Q)) measured from 2004 to 2013. In these models, 70% and 30% of the dataset were used for the training and testing stage for the reliability of the developed models. The precision of the models was optimized by investigating five different scenarios based on various time-lags combinations. Model’s performance has been assessed and evaluated using five statistical metrics, namely, correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). Results showed that Model-3 outperforms as compared to other proposed models. Machine learning models have been examined five scenarios of input variables during training and testing phases. In comparison of the Model-5 struggled in capturing the river's flow rate and showed poor performance in scenarios where R2 metric values ranged from 0.64 to 0.94. Therefore, it can be concluded that the RT model could be used as a robust model for sustainable flood plain management. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.},

note = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85125741738,

title = {Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm},

author = { N. Thi Thuy Linh and M. Pandey and S. Janizadeh and G. Sankar Bhunia and A. Norouzi and S. Ali and Q.B. Pham and D. Tran Anh and K. Ahmadi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125741738&doi=10.1016%2fj.asr.2022.02.027&partnerID=40&md5=17324924f450fcf0ae38b1cfd7d3aa7c},

doi = {10.1016/j.asr.2022.02.027},

issn = {02731177},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Advances in Space Research},

volume = {69},

number = {9},

pages = {3301-3318},

publisher = {Elsevier Ltd},

abstract = {Flood is the most common natural hazard that causing unprecedented loss of life and property in the world. In recent years, flood damage has increased due to human intervention and land use and climate changes. The purpose of this study is to predict flood susceptibility in Tafresh watershed in Markazi Province, Iran based on K-nearest neighbor (KNN), Extremely Gradient Boosting (XGB) machine learning models and evaluate the performance of hybrid genetic algorithm (GA) optimization method and XGB model. For this purpose, 14 independent variables affecting flood susceptibility were prepared, also, 227 flood locations were identified as independent variables based on available information and field survey. In order to evaluate the efficiency of the models, receiver operating characteristic (ROC) parameters were used. Evaluating the efficiency of the models based on the AUCs of testing dataset showed the higher efficiency of the GA-XGB hybrid model in modeling flood susceptibility in the Tafresh watershed is compared to KNN and XGB models, and the AUC in KNN, XGB, and GA-XGB models are 0.82, 0.85, and 0.87, respectively. So using the genetic algorithm as an optimizer for determining the best parameters in the XGB model increases efficiency in this model. The results of determining the relative importance of independent variables in flood susceptibility modeling showed that the independent variables considered in each model have different effects. Distance from road and distance from river in all three models had significant importance in modeling flood hazard in the Tafresh watershed. The optimization method in this study can be used as a powerful method in other spatial modeling studies. The results of this study also support management programs to reduce flood risks in the study area. © 2022 COSPAR},

note = {19},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85125948403,

title = {Applicability of machine learning techniques for multi-time step ahead runoff forecasting},

author = { T.S. Bajirao and A. Elbeltagi and M.S. Kumar and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125948403&doi=10.1007%2fs11600-022-00749-z&partnerID=40&md5=48a82e0d7b77fd29a08a3b202735ca53},

doi = {10.1007/s11600-022-00749-z},

issn = {18956572},

year  = {2022},

date = {2022-01-01},

journal = {Acta Geophysica},

volume = {70},

number = {2},

pages = {757-776},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t + 1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2) values of 168.94 m3/s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3/s, 0.67, 0.84, 0.7023 and 148.55 m3/s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85125503537,

title = {Random forest and nature-inspired algorithms for mapping groundwater nitrate concentration in a coastal multi-layer aquifer system},

author = { Q.B. Pham and D.A. Tran and N.T. Ha and A.R.M.T. Islam and R. Salam},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125503537&doi=10.1016%2fj.jclepro.2022.130900&partnerID=40&md5=c6165c3ec4b6ff8161f17eb17603c394},

doi = {10.1016/j.jclepro.2022.130900},

issn = {09596526},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Cleaner Production},

volume = {343},

publisher = {Elsevier Ltd},

abstract = {Accurate prediction of groundwater nitrate concentrations is critical for pollution control and sustainable groundwater resource management. However, Ant optimization (Ant), Firefly, Multiple Objective Evolutionary (MOE), and Grey Wolf Optimization (GWO), are novel data-driven hydrid algorithms that are barely used in groundwater pollution studies. Yet, areas susceptible to groundwater nitrate concentration have not been evaluated in the Mekong Delta. In this paper, we develop five novel hybrid algorithms: Ant, Firefly, MOE, GWO and Particle Swarm Optimization (PSO) which are integrated with the random forest (RF) algorithm for mapping groundwater nitrate concentrations in the coastal multi-aquifers of the Mekong Delta. The geographic information system (GIS) environment was used to construct 24 conditional factors believed to affect groundwater nitrate contamination. In 216 wells, nitrate concentrations were collected and quantified, and the results were utilized as a dependent parameter in modeling. To assess the effectiveness of these proposed hybrid models, evaluation measures such as statistical error indices, confusion matrices, Taylor diagram, the probability density function of error, and scatter plots were used. The RF-Firefly model (RMSE = 6.25; AUC = 0.961) performed the best based on testing datasets, followed by the RF-MOE (RMSE = 8.67; AUC = 0.878), the RF-Ant (RMSE = 7.27; AUC = 0.874), the RF-GWO ( RMSE = 7.35; AUC = 0.87), RF-PSO (RMSE = 10.24; AUC = 0.864) and RF ( RMSE = 15.85; AUC = 0.816) models. The results of mapping groundwater nitrate concentrations also showed that the south-central region had a very high nitrate concentration (> 45 mg/L) compared to other places. It was also found that extensive agricultural activities, wells near farming areas, and overexploitation of groundwater were the main causes of nitrate pollution in the study. The proposed models show their capability and application in predicting groundwater nitrate pollution in various deltaic locations. © 2022 Elsevier Ltd},

note = {14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85124827092,

title = {Evaluating the variability in long-term rainfall over India with advanced statistical techniques},

author = { I.A. Ahmed and R. Salam and M.W. Naikoo and A. Rahman and B. Praveen and P.N. Hoai and Q.B. Pham and D. Tran Anh and Q.T. Doan and I. Elkhrachy},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124827092&doi=10.1007%2fs11600-022-00735-5&partnerID=40&md5=a80566014555d992356af08082c04e76},

doi = {10.1007/s11600-022-00735-5},

issn = {18956572},

year  = {2022},

date = {2022-01-01},

journal = {Acta Geophysica},

volume = {70},

number = {2},

pages = {801-818},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85124100696,

title = {Flow structure investigation over a pool-riffle sequence in a variable width river},

author = { E. Shahiri Tabarestani and H. Afzalimehr and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124100696&doi=10.1007%2fs11600-021-00723-1&partnerID=40&md5=7ca61a73d6f1a6e2420f5d861dd0d479},

doi = {10.1007/s11600-021-00723-1},

issn = {18956572},

year  = {2022},

date = {2022-01-01},

journal = {Acta Geophysica},

volume = {70},

number = {2},

pages = {713-727},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {A comprehensive overview of flow characteristics in natural channels with bedforms is a vital issue in river management projects. Pool-riffle sequences as common bedforms in the gravel-bed rivers significantly impact flow characteristics and turbulence intensity. The present study was taken by field investigation in the Babolroud River, Iran. A 95 m reach with variable width was chosen in this river and velocity components and shear stress were obtained in different sections. Quadrant analysis was also applied to determine the dominant bursting event in the pool section. The results revealed a phase shift for stream-wise velocity, near-bed velocities, and bed shear stress versus bed profile. In the pool, vertical velocity components were oriented downward near the bed and upward near the water surface, while in the riffle section vectors were oriented towards the bed. The findings of quadrant analysis demonstrated the ejections and sweeps as a dominant event close to the bed and water surface, respectively. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.},

note = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85122148469,

title = {A modified approach to quantify aquifer vulnerability to pollution towards sustainable groundwater management in Irrigated Indus Basin},

author = { M. Umar and S.N. Khan and A. Arshad and R.A. Aslam and H.M.S. Khan and H. Rashid and Q.B. Pham and A.H. Nasır and R. Noor and K.M. Khedher and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122148469&doi=10.1007%2fs11356-021-17882-9&partnerID=40&md5=6563fab9a5d5765208e24d797997e5b3},

doi = {10.1007/s11356-021-17882-9},

issn = {09441344},

year  = {2022},

date = {2022-01-01},

journal = {Environmental Science and Pollution Research},

volume = {29},

number = {18},

pages = {27257-27278},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM). SWAT-CRM along with other thematic layers was combined using weightage overlay analysis to prepare the maps of groundwater vulnerability index (VI). The weight scale (w) and rating scale (r) were assigned based on a survey of available literature, and we then amended them using the analytical hierarchy process (AHP) and a probability frequency ratio (PFR) technique. Results depicted that the region under high groundwater vulnerability was found to be 5–22% using traditional recharge maps, while those are 9–23% using improved SWAT-CRM. The area under the curve (AUC) revealed that groundwater vulnerability zones predicted with SWAT-CRM outperformed the DRASTIC model applied with the traditional recharge map. Groundwater electrical conductivity (EC) was>2500 mS/cm in the high groundwater vulnerability zones, while it was <1000 mS/cm in the low groundwater vulnerability zones. The outcomes of this study can be used to improve the sustainability of the groundwater resources in IIB through proper land-use management practices. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85120546215,

title = {Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality},

author = { A. Masood and M. Aslam and Q.B. Pham and W. Khan and S. Masood},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120546215&doi=10.1007%2fs11356-021-17594-0&partnerID=40&md5=8aaa2af448d8e7ae547670924618ad67},

doi = {10.1007/s11356-021-17594-0},

issn = {09441344},

year  = {2022},

date = {2022-01-01},

journal = {Environmental Science and Pollution Research},

volume = {29},

number = {18},

pages = {26860-26876},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH; EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality. © 2021, The Author(s).},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85126908832,

title = {Efficiency of Geospatial Technology and Multi-Criteria Decision Analysis for Groundwater Potential Mapping in a Semi-Arid Region},

author = { A.M. Masoud and Q.B. Pham and A.K. Alezabawy and S.A. Abu El-Magd},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126908832&doi=10.3390%2fw14060882&partnerID=40&md5=74060f74b27011d2223fe3726fc9e751},

doi = {10.3390/w14060882},

issn = {20734441},

year  = {2022},

date = {2022-01-01},

journal = {Water (Switzerland)},

volume = {14},

number = {6},

publisher = {MDPI},

abstract = {The increasing water demand in Egypt causes massive stress on groundwater resources. The high variability in the groundwater depth, aquifer properties, terrain characteristics, and shortage of rainfall make it necessary to identify the groundwater potentiality in semi-arid regions. This study used the possibilities of multi-criteria decision approaches (MCDA), geographical information system (GIS), and groundwater field data to delineate potential groundwater zones in the Tushka area, west of Lake Nasser, South Egypt. Furthermore, groundwater potentiality identification can help decision-makers better plan and manage the water resources in this promising area. Eight controlling factors were utilized to achieve the objective of the present work using multi-criteria decision analysis (MCDA) approaches, namely the analytical hierarchy process (AHP) and frequency ratio (FR) models. The controlling parameters were integrated with the geographic information system (GIS) to develop the zones of groundwater potentialities. The results revealed that high and moderate-potential zones cover approximately 61% and 52% of the total area in the AHP and FR models, respectively. A total of 44 groundwater production wells along with the well yield were collected and used to validate the models. The results were evaluated using the receiver operating characteristics (ROC) curve. The best-performing prediction rates achieved by AHP and FR were 83% and 81%, respectively. Finally, the obtained results indicated that the AHP model achieved better performance than the FR model. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85125793028,

title = {Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model},

author = { F. Di Nunno and F. Granata and Q.B. Pham and G. de Marinis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125793028&doi=10.3390%2fsu14052663&partnerID=40&md5=75aa2500056c9ca975cbe672da9d8db0},

doi = {10.3390/su14052663},

issn = {20711050},

year  = {2022},

date = {2022-01-01},

journal = {Sustainability (Switzerland)},

volume = {14},

number = {5},

publisher = {MDPI},

abstract = {Precipitation forecasting is essential for the assessment of several hydrological processes. This study shows that based on a machine learning approach, reliable models for precipitation prediction can be developed. The tropical monsoon-climate northern region of Bangladesh, including the Rangpur and Sylhet division, was chosen as the case study. Two machine learning algorithms were used: M5P and support vector regression. Moreover, a novel hybrid model based on the two algorithms was developed. The performance of prediction models was assessed by means of evaluation metrics and graphical representations. A sensitivity analysis was also carried out to assess the prediction accuracy as the number of exogenous inputs reduces and lag times increases. Overall, the hybrid model M5P-SVR led to the best predictions among used models in this study, with R2 values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85118550599,

title = {Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches},

author = { K.M. Alkindi and K. Mukherjee and M. Pandey and A. Arora and S. Janizadeh and Q.B. Pham and D. Tran Anh and K. Ahmadi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118550599&doi=10.1007%2fs11356-021-17224-9&partnerID=40&md5=a66e9ec9a0abdfac8a2ea6066e080cd2},

doi = {10.1007/s11356-021-17224-9},

issn = {09441344},

year  = {2022},

date = {2022-01-01},

journal = {Environmental Science and Pollution Research},

volume = {29},

number = {14},

pages = {20421-20436},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R2 = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85121859068,

title = {Spatio-temporal calibration of Hargreaves–Samani model in the Northern Region of Nigeria},

author = { A.T. Ogunrinde and I. Emmanuel and M.A. Enaboifo and T.A. Ajayi and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121859068&doi=10.1007%2fs00704-021-03897-2&partnerID=40&md5=f9fc39371e21110403b9e6c149a74712},

doi = {10.1007/s00704-021-03897-2},

issn = {0177798X},

year  = {2022},

date = {2022-01-01},

journal = {Theoretical and Applied Climatology},

volume = {147},

number = {3-4},

pages = {1213-1228},

publisher = {Springer},

abstract = {One of the significant components of the hydrological cycle is evapotranspiration. Monthly meteorological parameters of 35 years from 19 meteorological stations across the Northern Region of Nigeria (NRN) were obtained and utilized for the calibration of Hargreaves–Samani (HS) model by comparing between potential evapotranspiration (ETo) values estimated from the original HS and the Penman–Monteith (FAO-56 PM) models. The calibrated HS equation was assessed using trend patterns and some statistical indices. The average value of root mean square error (RMSE) and the mean absolute error (MAE) decreased by 37.1 and 40%, respectively, after the calibration of the model. Also, the correlation coefficients (R) of stations that had values > 0.8 increased from 6 to 11 and the minimum R value increased by 12% above that of the original HS equation. The trend and spatial map of the statistical tests conducted also indicate better performance in most climatic regions after calibration. The precision of the HS equation improved significantly after calibration for semi-arid, humid, and sub-humid regions. However, few stations in the semi-arid, humid, and sub-humid regions did not show drastic improvement due to the peculiarity of the location and high variations in the wind speed and relative humidity parameters. © 2021, The Author(s).},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85121216717,

title = {Evaluating novel hybrid models based on GIS for snow avalanche susceptibility mapping: A comparative study},

author = { P. Yariyan and E. Omidvar and M. Karami and A. Cerdà and Q.B. Pham and J.P. Tiefenbacher},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121216717&doi=10.1016%2fj.coldregions.2021.103453&partnerID=40&md5=c2fdf98058691c669ea7ed22f32aa492},

doi = {10.1016/j.coldregions.2021.103453},

issn = {0165232X},

year  = {2022},

date = {2022-01-01},

journal = {Cold Regions Science and Technology},

volume = {194},

publisher = {Elsevier B.V.},

abstract = {Snow avalanches cause economic losses in many parts of the world, especially in mountainous areas. Due to its changing climates and the complex topography of mountainous regions in Iran, avalanches are significant problems. Therefore, modeling snow avalanches to predict them is essential for sustainable and safe development, appropriate infrastructure, planning for residential development, and loss prevention. This study assesses the performances of multi-criteria decision-making models and machine-learning hybrid models for snow avalanche susceptibility mapping in northwestern Iran's Sirvan watershed based on the weight of analytic network process, weighted linear combination, and frequency ratio. Using multicollinearity analysis and Pearson correlation, eleven snow avalanche conditioning factors were deemed appropriate for use. The areas under the curve (AUC), root mean square error (RMSE), seed cell area index, and snow avalanche relative index were used to validate snow avalanche susceptibility maps. The results showed that all hybrid models performed very well for the determination of the susceptibility of locations to avalanching. Based on the results of the validation analyses, the analytic network process-frequency ratio hybrid model was best among the models (AUC = 0.941; RMSE = 0.3). The resulting maps provide strong evidence to guide efforts to prevent the exposure to snow avalanches in the Sirvan watershed, and these hybrid models can be tested in other regions affected by snow avalanches. © 2021},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85135865100,

title = {Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness},

author = { S. Hadian and H. Afzalimehr and N. Soltani and E. Shahiri Tabarestani and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135865100&doi=10.1080%2f10106049.2022.2107714&partnerID=40&md5=0fff5c73ca9888aa7a64fceb3edf72f3},

doi = {10.1080/10106049.2022.2107714},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {27},

pages = {16283-16306},

publisher = {Taylor and Francis Ltd.},

abstract = {This research is analyzed the flood hazard using two ensemble models in ​​Mazandaran Province, Iran and assess how the experience contributed to community flood preparedness by household surveys. For this purpose, two approaches of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Attributive Border Approximation Area Comparison (MABAC) combined with Analytical Hierarchy Process (AHP) and weight of evidence (WOE) models were applied. Applying eight effective factors, including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood risk zoning map was prepared with the help of Geographic Information System. The area under the receiver operating characteristic curve (AUROC) for test sample set was 87.14% and 83.24% for TOPSIS-based and MABAC-based models, respectively. Regression analysis found a negative correlation between past flood experience and flood preparedness of residents, consistent with cluster analysis. This indicates that local people had not learned much from previous experiences of flood catastrophes. © 2022 Informa UK Limited, trading as Taylor & Francis Group.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85130149381,

title = {Assessment of spatio-temporal trends of satellite-based aerosol optical depth using Mann–Kendall test and Sen’s slope estimator model},

author = { L. Mohammad and I. Mondal and J. Bandyopadhyay and Q.B. Pham and X.C. Nguyen and C.D. Dinh and A. Al-Quraishi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130149381&doi=10.1080%2f19475705.2022.2070552&partnerID=40&md5=dd9fcc8bc0d997c8b1d0d1aa9bae6078},

doi = {10.1080/19475705.2022.2070552},

issn = {19475705},

year  = {2022},

date = {2022-01-01},

journal = {Geomatics, Natural Hazards and Risk},

volume = {13},

number = {1},

pages = {1270-1298},

publisher = {Taylor and Francis Ltd.},

abstract = {Aerosols are an inextricably linked component of the atmosphere. Nowadays the study of aerosols has attracted the attention of the world community due to the increasing concerns over air pollution and climate change. Aerosol optical depth (AOD) is the measure of aerosols distributed within the atmospheric column from the Earth's surface to the top of the atmosphere. This study was conducted to examine the trend in AOD between latitudes 22° and 24.62° N, and longitudes 83.26° and 87.01° E, covering the entire part of the Indian state of Jharkhand. Mann–Kendall (MK) trend test and Sen’s slope estimator model were used to examining the trend over 18 years (period: 2000–2017) AOD data obtained from satellite-based sensor namely MODIS. The highest AOD was observed in the north-eastern part, while the lowest was observed in the state’s southwestern part. The mean relative percentage change (RPC) analysis showed that the AOD increased from 20 to 60%. Jharkhand State comprises various sub-regions; all the sub-regions, including major cities, have shown a remarkable positive trend. In particular, Dhanbad, Sahibganj, Chaibasa, Jamshedpur, Ranchi, and Hazaribagh demonstrate statistically significant positive trends (99% confidence level). It was observed that the highest positive trend (0.1228) and the lowest negative trend (−0.02587) were in Sahibganj and Gumla districts, respectively. This study revealed a statistically robust significant correlated pattern of AOD with the variability of meteorological factors. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.},

note = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85129655095,

title = {Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling},

author = { M.T. Riaz and M. Basharat and Q.B. Pham and Y. Sarfraz and A. Shahzad and K.S. Ahmed and N. Ikram and M.S. Waseem},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129655095&doi=10.1080%2f10106049.2022.2066202&partnerID=40&md5=f77892c8df6eb0e54f8e77d99a0e5c29},

doi = {10.1080/10106049.2022.2066202},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {26},

pages = {12294-12337},

publisher = {Taylor and Francis Ltd.},

abstract = {Landslide predictive performance is expected to vary with different sampling techniques, such as landslide random and cluster sampling. Current advancements in remote sensing technologies and machine learning (ML) have enhanced landslide prediction performance. The Himalayan Mountain range in Pakistan poses an unadorned threat to the ecosystem and valley population because of landslide occurrence. The present study explores, and tests alternative sampling technique based on spatial pattern characterization in the wake of increased landslide prediction efficacy, rather than a renowned random technique for training and testing sampling. Thereupon, landslide inventory data with 17 geo-environmental factors (i.e. topographic; hydrological and seismic factors) were determined. Landslide cluster patterns were confirmed by the Nearest Neighbor Index (NNI) method and after getting the cluster patterns, the predicted performance of landslide sampling was tested using ML and statistical methods. Advanced ML algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), K-nearest Neighbors (KNN) and statistical methods including Weight-of-Evidence (WofE) and Logistic Regression (LR) were used and validated. The landslide-prone district of Azad Jammu and Kashmir (Neelum Valley), Kashmir Himalayas, Pakistan, was selected as a case study. Prediction performance rates are high with area under the curve (AUC) ranging from 0.802 to 0.912; accuracy (ACC) ranges from 0.78 to 0.89, and kappa ranges from 0.50 to 0.68 with cluster sampling technique, whereas the performance was low with random sampling technique, with AUC ranges from 0.768 to 0.895; ACC ranges from 0.74 to 0.86 and kappa ranges from 0.48 to 0.64. The descending order of accuracy of the six algorithms was XGboost, RF, KNN, NB, LR and WofE. Our results confirmed that the landslides followed cluster patterns in the study area, and ML algorithms with cluster training samples positively affected landslide susceptibility prediction with a statistically significant difference. The outcomes support the hypothesis that using landslides spatial natural existence, as training samples, instead of random concepts, improves the prediction ability; and highlights that alternative landslide partitioning technique could be a practicable and robust choice for landslides prediction modelling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.},

note = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85127095456,

title = {Evaluating the impact of the environment on depleting groundwater resources: a case study from a semi-arid and arid climatic region},

author = { K.U. Rahman and A. Hussain and N. Ejaz and M.N. Shahid and Z. Duan and B. Mohammadi and P.N. Hoai and Q.B. Pham and K.M. Khedher and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127095456&doi=10.1080%2f02626667.2022.2044483&partnerID=40&md5=69f2c1441c8cad417c4f6291498345d8},

doi = {10.1080/02626667.2022.2044483},

issn = {02626667},

year  = {2022},

date = {2022-01-01},

journal = {Hydrological Sciences Journal},

volume = {67},

number = {5},

pages = {791-805},

publisher = {Taylor and Francis Ltd.},

abstract = {This study, for the first time, assesses the impact of critical environmental factors on groundwater using Bayesian network (BN) integrated with analytical hierarchy process (AHP) and develops a groundwater vulnerability map. The considered environmental factors are divided into the following categories: physical (rainfall; temperature (Tmax/Tmin); relative humidity (RHmax/RHmin)), water use and demand (surface water availability (SW) and number of tubewells (NTW)), agriculture and land use (total irrigated area (TIA); total cropped area (TCA); and total area sown (TAS)), and population. Results show a negative relationship of rainfall, RHmax/RHmin, and SW and a positive relationship of the remaining variables with groundwater. Elasticities demonstrate that a 1% change in SW (rainfall), major contributors, results in a decrease by 0.64% (0.55%) in groundwater in Bahawalpur (Multan). A 1% change in population (NTW), major consumers, results in an increase by 0.74% (0.70%) in depth to water table (DWT) across Jhang (Khanewal). The vulnerability map shows that high and very high vulnerability classes account for more than 50% of the total area of Punjab. © 2022 IAHS.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85125148404,

title = {Multi attributive ideal-real comparative analysis (MAIRCA) method for evaluating flood susceptibility in a temperate Mediterranean climate},

author = { S. Hadian and E. Shahiri Tabarestani and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125148404&doi=10.1080%2f02626667.2022.2027949&partnerID=40&md5=ccd9dc9eb62db90342db3a2522d0cde8},

doi = {10.1080/02626667.2022.2027949},

issn = {02626667},

year  = {2022},

date = {2022-01-01},

journal = {Hydrological Sciences Journal},

volume = {67},

number = {3},

pages = {401-418},

publisher = {Taylor and Francis Ltd.},

abstract = {This study is focused on flood susceptibility evaluation across the Golestan Province, Iran, using novel ensemble models generated by Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) with frequency ratio (FR) and weight of evidence (WOE). As MAIRCA was employed in flood susceptibility assessment for the first time, an attempt has been made to evaluate its capability by comparing the ensemble with multilayer perceptron (MLP) neural network-based models. Ten flood conditioning factors (altitude; slope; aspect; plan curvature; distance from rivers; topographic wetness index; rainfall; soil type; geology; and land use) and 240 flood and non-flood locations were applied for modelling, of which 70% were selected for training and 30% for validation. The results of validation, performed by the receiver operating characteristics curve method, indicate that the highest predictive accuracy was obtained by MLP-WOE (0.926), followed by MLP-FR (0.912), MAIRCA-WOE (0.885), and MAIRCA-FR (0.859). High precision of the models implies their capability in flood risk prediction. © 2022 IAHS.},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85124366508,

title = {Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method},

author = { A. Saleh and A. Yuzir and N. Sabtu and S.K.M. Abujayyab and M.R. Bunmi and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124366508&doi=10.1080%2f10106049.2022.2032394&partnerID=40&md5=e2e0069c77e7461241e15065cdeefb2c},

doi = {10.1080/10106049.2022.2032394},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {25},

pages = {10199-10228},

publisher = {Taylor and Francis Ltd.},

abstract = {Flooding is the main recurring natural disaster in Sungai Pinang catchment, Malaysia. Flash flood susceptibility mapping (FFSM) explains a key component of flood risk analysis and enables efficient estimation of the spatial extent of flood characteristics. The current study applied four machine learning models (i.e. Logistic Regression (LR); K-Nearest Neighbors (KNN); Random Forest (RF); and Extreme Gradient Boosting (XGBoost)) ensembled with the Statistical Index (SI) to develop flash flood susceptibility mapping (FFSM). 110 flash flood locations in the Sungai Pinang catchment were used in this study. Genetic algorithm (GA) was combined with Fuzzy Unordered Rules Induction Algorithm (FURIA), Rotation Forest, and Random Subspace for the feature selection method (FSM). The results showed that GA-FURIA outperformed the other two models in terms of accuracy based on the FSM. Twelve flash flood variables were selected by GA-FURIA. The FFSM results showed that the SI-RF model has the highest area under the receiver operating characteristics (AUROC) curve of success rate (0.978), whereas the SI-XGB has the best AUROC in terms of validation rate (0.997). The findings suggest that the twelve ideal conditioning variables may be used to optimize FFSM development. © 2022 Informa UK Limited, trading as Taylor & Francis Group.},

note = {9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85122310778,

title = {Evaluation of debris flow and landslide hazards using ensemble framework of Bayesian- and tree-based models},

author = { S.C. Pal and R. Chakrabortty and A. Saha and S.K. Bozchaloei and Q.B. Pham and N.T.T. Linh and D. Tran Anh and S. Janizadeh and K. Ahmadi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122310778&doi=10.1007%2fs10064-021-02546-2&partnerID=40&md5=28e590545f4407e570d937b8e313d318},

doi = {10.1007/s10064-021-02546-2},

issn = {14359529},

year  = {2022},

date = {2022-01-01},

journal = {Bulletin of Engineering Geology and the Environment},

volume = {81},

number = {1},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The modeling and prediction of land movement susceptibility hazards, i.e., debris flow, landslide, and rock fall, can assist in controlling and preventing a variety of societal and environmental damages. The purpose of this study was to develop a land movement susceptibility hazard model of debris flow, landslide, and multiple land movement, i.e., combination of debris flow and landslide in the Saveh city of Markazi Province, Iran, using an ensemble of Bayesian generalized linear model (BGLM), sparse partial least squares (SPLS), boosted tree (BT), and random forest (RF) algorithms. For this purpose, 167 debris flow points, 261 landslide points, and 257 multiple (debris flow and landslide) points were identified based on field visits and available information, and 15 suitable conditioning factors were prepared as independent variables for this study. The accuracy and efficiency of the models were assessed using the receiver operating characteristic (ROC) and other statistical indices. The variable importance result indicates that slope is the most important factor in debris flow (25.53), landslide (31.39), and multiple hazard (41.90) occurrences. The accuracy assessment results in the validation phase revealed that the RF is the most optimal among the applied algorithms, with area under the curve (AUC) values of 0.90, 0.94, and 0.89 for debris flow, landslide, and multiple (D + L) hazard modeling. The findings of this study indicated that the use of a Bayesian and tree-based ensemble model in preparing a land movement-related disaster map could be useful among policymakers and land use planners for sustainable land use management and practices. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {15},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85121873630,

title = {Groundwater flow modeling in the basaltic hard rock area of Maharashtra, India},

author = { C.B. Pande and K.N. Moharir and S.K. Singh and A. Elbeltagi and Q.B. Pham and B. Panneerselvam and A.M. Varade and S. Kouadri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121873630&doi=10.1007%2fs13201-021-01525-y&partnerID=40&md5=e8864cacbf0a2a7307f892cafe5f1c75},

doi = {10.1007/s13201-021-01525-y},

issn = {21905487},

year  = {2022},

date = {2022-01-01},

journal = {Applied Water Science},

volume = {12},

number = {1},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The ecological sustainable development and planning of groundwater resources is an excessive challenge for many countries currently facing water insufficiency. The main focus of this work was to determine the direction of groundwater flow, head value, and water level using the steady-state finite difference model (MODFLOW software) in basaltic formations in Maharashtra, India. The MODFLOW model was integrated with ground data using Geographic Information System (GIS) for sustainable groundwater resource management in the hard rock terrain. The MODFLOW-2005 model simulated the interaction between heads and time in 2014–18 by steady-state conditions. In this present study, four observation wells were selected. During the field survey, four observation wells have been monitored regularly as per the Central Groundwater Board guidelines. MODFLOW software has been conceptualized as a double-layered rigid and fractured aquifer area feast over 18,312 m × 11,265 m area. This research demonstrates that the integration of GIS, conventional fieldwork, and mathematical model can support to understand groundwater demand and supply in a better way. © 2021, The Author(s).},

note = {24},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85121391168,

title = {Current and future projections of flood risk dynamics under seasonal precipitation regimes in the Hyrcanian Forest region},

author = { Q.B. Pham and S.C. Pal and A. Saha and I. Chowdhuri and J.A. Albanai and S. Janizadeh and K. Ahmadi and K.M. Khedher and D. Tran Anh and W. Duan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121391168&doi=10.1080%2f10106049.2021.2009921&partnerID=40&md5=826f65f09af731d2898590fbfb8be6a1},

doi = {10.1080/10106049.2021.2009921},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {25},

pages = {9047-9070},

publisher = {Taylor and Francis Ltd.},

abstract = {The main aim of this research is to predict the impact of seasonal precipitation regimes on flood hazard applying machine learning models. For this purpose, twelve static variables and eight rainfall dynamics variables for 2050s (RCP 2.6 and 8.5) were used as conditioning factors. Four machine learning algorithms including K-Nearest Neighbour (KNN), Extremely Randomize Trees (ERT), Random Forest (RF) and Oblique-Random Forest (ORF) were used to model flood risk. Considering the area under curve (AUC) and other indices, the ORF was the most optimal model. The AUC of the KNN, ERT, RF and ORF for the validation datasets were 0.85, 0.90, 0.89 and 0.92 respectively. The results showed that under two RCPs, spatial distribution of high flood risk areas will change in the future and the trends will be different from the current. These results could provide valuable insights in simulating, predicting and reducing future flood risk. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85120085115,

title = {Consideration of spatial heterogeneity in landslide susceptibility mapping using geographical random forest model},

author = { R.P. Quevedo and D.A. Maciel and T.D.T. Uehara and M. Vojtek and C.D. Rennó and B. Pradhan and J. Vojteková and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120085115&doi=10.1080%2f10106049.2021.1996637&partnerID=40&md5=1562e2d5154afb7a80cdb7e473d146b2},

doi = {10.1080/10106049.2021.1996637},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {25},

pages = {8190-8213},

publisher = {Taylor and Francis Ltd.},

abstract = {Most previous studies of landslide susceptibility mapping (LSM) have not contemplated spatial heterogeneity and the commonly used models for LSM are aspatial, which could reduce model performance. Therefore, aiming to evaluate the applicability of spatial algorithms to predict landslide susceptibility, the performance of geographical random forest (GRF) was evaluated, in comparison to random forest (RF) and extreme gradient boosting (XGBoost). Based on the results, GRF presented the better performance (AUC = 0.876), followed by RF (AUC = 0.748) and XGBoost (AUC = 0.745). GRF also provided the most suitable susceptibility map. While RF and XGBoost presented almost 50% of the study area as susceptible, the GRF presented more concentrated susceptibility areas spatially, with a reasonable area for moderate (15.55%), high (8.73%) and very-high (2.59%) susceptibility classes. Finally, it can be inferred that spatial assessment may improve model performance, and that spatial models have a great potential for LSM. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {7},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85119691155,

title = {Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling},

author = { S.I. Abba and R.A. Abdulkadir and S.S. Sammen and Q.B. Pham and A.A. Lawan and P. Esmaili and A. Malik and N. Al-Ansari},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119691155&doi=10.1016%2fj.asoc.2021.108036&partnerID=40&md5=2a481a24fce5895904e1bf4a02a5bdd8},

doi = {10.1016/j.asoc.2021.108036},

issn = {15684946},

year  = {2022},

date = {2022-01-01},

journal = {Applied Soft Computing},

volume = {114},

publisher = {Elsevier Ltd},

abstract = {The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffe efficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to the results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018; MSE = 10.5195/ 9.7889 mg/L; RMSE = 3.2434/ 3.1287 mg/L; MAPE = 3.8032/ 3.0348 mg/L; and CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site. © 2021 Elsevier B.V.},

note = {14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85119413143,

title = {Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning},

author = { R. Costache and Q.B. Pham and A. Arabameri and D.C. Diaconu and I. Costache and A. Crăciun and N. Ciobotaru and M. Pandey and A. Arora and S.A. Ali and B.T. Pham and H.A. Nguyen and H.A. Tuan and M. Avand},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119413143&doi=10.1080%2f10106049.2021.2001580&partnerID=40&md5=ef28c65b84b3d87e24b8fecebfe23399},

doi = {10.1080/10106049.2021.2001580},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {25},

pages = {8361-8393},

publisher = {Taylor and Francis Ltd.},

abstract = {The present study aims to enrich the specialized literature by proposing and calculating a new flash-flood propagation susceptibility index (FFPSI). Thus, firstly the Flash-Flood Potential Index (FFPI) using the ensembles of the next models was calculated: Weights of Evidence (WOE), Analytical Hierarchy Process (AHP), Logistic Regression (LR), Classification and Regression Trees (CART), and Radial Basis Function Neural Network-Weights of Evidence (RBFN-WOE). A number of 255 flash-flood locations, split into training (70%) and validating (30%) samples, along with 10 predictors were used as input in the five models. The Receiver Operating Characteristics (ROC) Curve and several statistical metrics were used to evaluate the Flash-Flood Potential Index results. LR-WOE and AHP-WOE were the most performant models. Nevertheless, all the applied models performed very well (AUC > 0.85). Further, the FFPSI was determined by integrating the FFPI results into a Flow Accumulation procedure. Over 55% of the valleys identified are characterized by high and very high values of FFPSI. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85117242614,

title = {Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes},

author = { Q.B. Pham and S.C. Pal and R. Chakrabortty and A. Saha and S. Janizadeh and K. Ahmadi and K.M. Khedher and D. Tran Anh and J.P. Tiefenbacher and A. Bannari},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117242614&doi=10.1080%2f10106049.2021.1986579&partnerID=40&md5=287098038469114f2c4b4c933e6ed6d6},

doi = {10.1080/10106049.2021.1986579},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {25},

pages = {7881-7907},

publisher = {Taylor and Francis Ltd.},

abstract = {Landslides are most catastrophic and frequently occurred across the world. In mountainous areas of the globe, recurrent occurrences of landslide have caused huge amount of economic losses and a large number of casualties. In this research, we attempted to estimate the potential impact of climate and LULC on future landslide susceptibility in of Markazi Province of Iran. We considered the boosted tree (BT), random forest (RF) and extremely randomized tree (ERT) models for landslide susceptibility assessment in Markazi Province. The results of evaluation criteria showed that ERT model is most optimal than other models used in this study with AUC values of 0.99 and 0.93 for the training and validation datasets, respectively. According to the ERT model, the spatial coverage of the very high and high land slide susceptible zones for the current period, 2050s considering RCP 2.6 and 2050s considering RCP 8.5 are 428.5 km2, 439.6 km2 and 465.2 km2, respectively. From this analysis it is clear that the impact of climate and LULC changes on future landslide susceptibility is prominent. The results of the present study help managers to reduce landslide damages, not only for current but also for future conditions, based on climate and LULC changes. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85117041491,

title = {Daily precipitation concentration in Central Coast Vietnam},

author = { A. Benhamrouche and J. Martin-Vide and Q.B. Pham and M.E. Kouachi and M.C. Moreno-Garcia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117041491&doi=10.1007%2fs00704-021-03804-9&partnerID=40&md5=83c30f7a73762225e5b66c450ebe4d94},

doi = {10.1007/s00704-021-03804-9},

issn = {0177798X},

year  = {2022},

date = {2022-01-01},

journal = {Theoretical and Applied Climatology},

volume = {147},

number = {1-2},

pages = {37-45},

publisher = {Springer},

abstract = {Empirical frequency distribution of daily precipitation amounts can be fitted by a negative exponential distribution, because anywhere there are many small daily totals and few large ones. Therefore, the cumulative percentages of days with precipitation, sorted in increasing order according to their amounts, against the cumulative percentage of the rainfall amounts that they contribute are fitted by positive exponential curves Y = aXebx, a and b constants. Based on these curves, the Concentration Index (CI) evaluates the contribution of the rainiest days to the total amount. In this study the CI has been calculated for 15 meteorological stations in Da Nang city and Quang Nam province in Central Coast Vietnam, for the 1979–2016 period. The results show high values of CI, ranging from 0.62 to 0.72. Conversely, the linear correlation between altitude and CI is negative (R = -0.60; p < 0.01). There are no correlations between the latitude nor the annual mean number of precipitation days and the CI. CI change for the sub-periods of 1979–1997 and 1998–2016 is also analyzed. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85116568518,

title = {Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms},

author = { X.C. Nguyen and T.T.H. Nguyen and Q.V. Le and P.C. Le and A.L. Srivastav and Q.B. Pham and P.M. Nguyen and D.D. La and E.R. Rene and H.H. Ngo and S.W. Chang and D.D. Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116568518&doi=10.1016%2fj.jenvman.2021.113868&partnerID=40&md5=464570f1cc01b2431c75203b6ec53a41},

doi = {10.1016/j.jenvman.2021.113868},

issn = {03014797},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Environmental Management},

volume = {301},

publisher = {Academic Press},

abstract = {Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009–2020 included 618 sets and 10 features. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L−1 for NH4–N and COD; respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH4–N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed. © 2021 Elsevier Ltd},

note = {14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85114596279,

title = {Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network},

author = { R. Costache and S.A. Ali and F. Parvin and Q.B. Pham and A. Arabameri and H.A. Nguyen and A. Crăciun and D. Tran Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114596279&doi=10.1080%2f10106049.2021.1973115&partnerID=40&md5=01a0f595a0d481212f49ac19b947f108},

doi = {10.1080/10106049.2021.1973115},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {25},

pages = {7303-7338},

publisher = {Taylor and Francis Ltd.},

abstract = {This article is intended to assess the flood-induced landslide susceptibility in the Indian state of Assam. This study area has high frequency and severity of landslides that are triggered by heavy rainfall and floods. In order to obtain the results, two machine learning models (XGBoost and DLNN) and one of the fuzzy-multi-criteria decision-making methods (FAHP) were combined with certainty factor (CF) bivariate statistic model. Firstly, 16 landslide predictors and 198 flood-induced landslide locations were prepared, this data set being split into training (70%) and validating data sets (30%). The analysis of the results shows that the region’s most prone to flood-induced landslide occurrence can be found in the southern part, while those less prone to these phenomena are generally located in the northern part of the study area. Receiver Operating Characteristic (ROC) curve indicator shows that XGBoost-CF is the most performance model (area under the curve [AUC] = 0.977), followed by FAHP-CF (AUC = 0.976), DLNN-CF (AUC= 0.974) and CF (AUC = 0.963). © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85110988300,

title = {Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree},

author = { R. Costache and A. Arabameri and H. Moayedi and Q.B. Pham and M. Santosh and H.A. Nguyen and M. Pandey and B.T. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110988300&doi=10.1080%2f10106049.2021.1948109&partnerID=40&md5=8603519fefbaf8d25d9e10c026dc7ee3},

doi = {10.1080/10106049.2021.1948109},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {23},

pages = {6780-6807},

publisher = {Taylor and Francis Ltd.},

abstract = {Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slope surfaces with a high potential for flash-flood employing a combination of fuzzy logic algorithm with the following four machine learning models: classification and regression tree, deep learning neural network, XGBoost and naïve Bayes. Ten flash-flood predictors were used as independent variables to determine the flash-flood potential index. As a dependent variable, we used areas with ttorrential phenomena divided into training (70%) and validating data set (30%). Predictive ability and the degree of correlation between factors were assessed through the correlation-based feature selection (CFS) method and through the confusion matrix, respectively. In the training phase, all ensemble models yielded good and very good accuracies of over 84%. The spatialization of flash-flood potential index (FFPI) over the study area showed that high and very high values of flash-flood potential occur in the northern half of the region and occupy the following weights within the study area: 53.11% (FFPI Fuzzy-CART), 45.09% (Fuzzy-DLNN), 45.58% (Fuzzy-NB) and 44.85% (Fuzzy-XGBoost). The validation of the results was done through the ROC curve method. Thus, according to success rate, Fuzzy-XGBoost (AUC = 0.886) is the best model, while in terms of prediction rate, the ideal one is Fuzzy-DLNN (AUC = 0.84). The novelty of this work is the application of the four ensemble models in evaluating this natural hazard. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {16},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85108184760,

title = {Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation},

author = { B. Mohammadi and R. Moazenzadeh and Q.B. Pham and N. Al-Ansari and K.U. Rahman and D. Tran Anh and Z. Duan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108184760&doi=10.1016%2fj.asej.2021.05.012&partnerID=40&md5=e04c817e358e3ebd07c0a629f8c53987},

doi = {10.1016/j.asej.2021.05.012},

issn = {20904479},

year  = {2022},

date = {2022-01-01},

journal = {Ain Shams Engineering Journal},

volume = {13},

number = {1},

publisher = {Ain Shams University},

abstract = {Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model; RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model; RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs ​​of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended. © 2021 THE AUTHORS},

note = {9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85106261605,

title = {Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees},

author = { R. Abedi and R. Costache and H. Shafizadeh-Moghadam and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106261605&doi=10.1080%2f10106049.2021.1920636&partnerID=40&md5=798fa231218f92d7d8e6ef1ce3c01241},

doi = {10.1080/10106049.2021.1920636},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {19},

pages = {5479-5496},

publisher = {Taylor and Francis Ltd.},

abstract = {Historical exploration of flash flood events and producing flash-flood susceptibility maps are crucial steps for decision makers in disaster management. In this article, classification and regression tree (CART) methodology and its ensemble models of random forest (RF), boosted regression trees (BRT) and extreme gradient boosting (XGBoost) were implemented to create a flash-flood susceptibility map of the Bâsca Chiojdului River Basin, one of the areas in Romania that is constantly exposed to flash floods. The torrential areas including 962 flash flood events were delineated from orthophotomaps and field observations. Furthermore, a set of conditioning forces to explain the flash floods was constructed which included aspect, land use and land cover (LULC), hydrological soil groups lithology, slope, topographic wetness index (TWI), topographic position index (TPI), profile curvature, convergence index and stream power index (SPI). All models indicated the slope as the most important factor triggering the flash flood occurrence. The highest area under the curve (AUC) was achieved by the RF model (AUC = 0.956), followed by the BRT model (AUC = 0.899), XGBoost model (AUC = 0.892) and CART model (AUC = 0.868), respectively. The results showed that the central part of the Bâsca Chiojdului river basin, which covers approximately 30% of the study area, is more susceptible to flash flooding. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {50},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85102922157,

title = {Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region},

author = { Y. Chen and W. Chen and S. Janizadeh and G.S. Bhunia and A. Bera and Q.B. Pham and N.T.T. Linh and A.L. Balogun and X. Wang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102922157&doi=10.1080%2f10106049.2021.1892212&partnerID=40&md5=c07a8225880c124b52f4ed8fb0d9a916},

doi = {10.1080/10106049.2021.1892212},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {16},

pages = {4628-4654},

publisher = {Taylor and Francis Ltd.},

abstract = {Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude; slope; plan curvature; profile curvature; distance from river; drainage density; distance from road; rainfall; land use; soil type; bulk density; CEC; pH; clay; silt; sand; topographical position index (TPI); topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model’s ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {24},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85100945647,

title = {Optimization of statistical and machine learning hybrid models for groundwater potential mapping},

author = { P. Yariyan and M. Avand and E. Omidvar and Q.B. Pham and N.T.T. Linh and J.P. Tiefenbacher},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100945647&doi=10.1080%2f10106049.2020.1870164&partnerID=40&md5=26f78eae2271160a277d5f453109d6aa},

doi = {10.1080/10106049.2020.1870164},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {13},

pages = {3877-3911},

publisher = {Taylor and Francis Ltd.},

abstract = {Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrinehroud watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms–frequency ratio (FR), radial basis function (RBF), index of entropy (IOE), evidential belief function (EBF) and fuzzy art map (FAM). To accomplish this, 1448 well locations in the study area were randomly divided into two data sets for training (70%= 1013 locations) and validation (30%= 435 locations) based on the holdout method. Fourteen factors that can affect the presence or absence of groundwater were identified, measured, and mapped using ArcGIS and SAGA-GIS software. The models were used to predict the locations of groundwater based on suitable combinations of the conditioning factors to produce groundwater potential maps. The probability of groundwater at any location was classified as low, moderate, high, or very high based on natural breaks in the data spectrum. The model predictions were tested for validity and their success was determined using receiver operating characteristic (ROC) curves, standard errors (SE), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF) and accuracy (ACC), and the Friedman test. The performance assessments of groundwater potential predictions using the area under the curve (AUC) and accuracy (ACC) showed that the FR-RBF model had very good performance (AUC= 0.889; ACC= 87.51). FR-FAM (AUC= 0.869; ACC= 84.67), EBF-FAM (AUC= 0.864; ACC= 84.42), EBF-RBF (AUC= 0.854; ACC= 83.94), FR-IOE (AUC= 0.836; ACC= 83.62), and EBF-IOE (AUC= 0.833; ACC= 80.42) also had acceptable performance. The results of the Friedman test also show that there are significant differences between the models and the highest mean rank was generated by the FR-FAM model (3.642). Therefore, the hybrid models can be used to increase the accuracy of groundwater-prediction models in the study region and perhaps in similar settings. Highlights The groundwater potential was studied in the area of Zarrinehroud watershed A combination of methods including FR, RBF, IOE, EBF and FAM Very high and high groundwater potential areas were located in the northern half The development of hybrid models can increase the accuracy of the results. © 2021 Informa UK Limited, trading as Taylor & Francis Group.},

note = {12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85097937024,

title = {Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility},

author = { M.B. Hassangavyar and H.E. Damaneh and Q.B. Pham and N.T.T. Linh and J.P. Tiefenbacher and Q.V. Bach},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097937024&doi=10.1080%2f10106049.2020.1837257&partnerID=40&md5=1ff2e4ea6eb5b7fd219fcfba5d788628},

doi = {10.1080/10106049.2020.1837257},

issn = {10106049},

year  = {2022},

date = {2022-01-01},

journal = {Geocarto International},

volume = {37},

number = {10},

pages = {2772-2794},

publisher = {Taylor and Francis Ltd.},

abstract = {This study tests the applicability of three resampling methods (i.e. bootstrapping; random-subsampling and cross-validation) for enhancing the performance of eight machine-learning models: boosted regression trees, flexible discriminant analysis, random forests, mixture discriminate analysis, multivariate adaptive regression splines, classification and regression trees, support vector machines and generalized linear models, compared to the use of the original data. The results of models were evaluated using correlation (COR), area under curve (AUC), true skill statistic (TSS), receiver-operating characteristic and the probability of detection (POD). The evaluation showed that the bootstrapping technique improved the performance of all models. The Bootstrapping-random forest (with COR = 0.75; AUC = 0.92; TSS = 0.80 and POD = 0.98) proved to be the best model for landslide prediction. Among the 18 contributing factors, distance from fault, curvature and precipitation were the most influential in all 32 models.Highlights Hazard prediction of landslide by the 8 machine-learning (ML) models. Multiple morphometric, climatic, geologic, vegetation and human factors were used. Tests the applicability of three resampling methods. The performance of the ML models and coupling models were assessed. © 2020 Informa UK Limited, trading as Taylor & Francis Group.},

note = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85121365223,

title = {Estimating yield and water productivity of tomato using a novel hybrid approach},

author = { H. Dehghanisanij and S. Emami and M. Achite and N.T.T. Linh and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121365223&doi=10.3390%2fw13243615&partnerID=40&md5=29427366664f415cbe3926ef79bd124a},

doi = {10.3390/w13243615},

issn = {20734441},

year  = {2021},

date = {2021-01-01},

journal = {Water (Switzerland)},

volume = {13},

number = {24},

publisher = {MDPI},

abstract = {Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation—fertilizer parameters (PMDI; F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85120946149,

title = {Development of boosted machine learning models for estimating daily reference evapotranspiration and comparison with empirical approaches},

author = { S. Mehdizadeh and B. Mohammadi and Q.B. Pham and Z. Duan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120946149&doi=10.3390%2fw13243489&partnerID=40&md5=ecac365c12b9231d9284baf35146420a},

doi = {10.3390/w13243489},

issn = {20734441},

year  = {2021},

date = {2021-01-01},

journal = {Water (Switzerland)},

volume = {13},

number = {24},

publisher = {MDPI},

abstract = {Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85120639492,

title = {Multifractal characterization and cross correlations of reference evapotranspiration time series of India},

author = { S. Adarsh and L.J. Nityanjaly and Q.B. Pham and R. Sarang and M. Ali and P. Nandhineekrishna},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120639492&doi=10.1140%2fepjs%2fs11734-021-00325-4&partnerID=40&md5=7e2a587cc293d7b79b9e76472aef32f9},

doi = {10.1140/epjs/s11734-021-00325-4},

issn = {19516355},

year  = {2021},

date = {2021-01-01},

journal = {European Physical Journal: Special Topics},

volume = {230},

number = {21-22},

pages = {3845-3859},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This study performs the multifractal characterization of reference evapotranspiration (ET0) and its controlling factors of five locations in India with climatic diversity. First, the ET0 and the predictor variables like minimum air temperature (Tmin) , maximum air temperature (Tmax) and average wind speed (AW) of five stations are analysed using multifractal detrended fluctuation analysis (MFDFA). The investigation could detect long-term persistence and multifractality of different series, irrespective of the climatic condition and geographical location. Higher persistence (>0.8) is noted in the ET0 and Tmin series indicating higher predictability in all the stations and highest multifractality was noted for the highly complex wind speed time series. Further, the ET0 estimates by Hargreaves Samani (HS) and Droogers and Allen (DA) methods differs in their persistence and multifractal properties, controlled by the geographic location of the station. Subsequently, multifractal cross correlation analysis (MFCCA) is used to investigate the correlations between ET0 and other variables. MFCCA analysis showed that, for all the time series considered, the joint scaling exponent is roughly the average of individual scaling exponents, and base width of the joint spectra is lower than that of individual series, validating two universal properties of multifractal cross correlation studies for agro-meteorological datasets. © 2021, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85118755975,

title = {Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques},

author = { C.B. Pande and K.N. Moharir and B. Panneerselvam and S.K. Singh and A. Elbeltagi and Q.B. Pham and A.M. Varade and J. Rajesh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118755975&doi=10.1007%2fs13201-021-01522-1&partnerID=40&md5=e50f37e10b85a07248010f167538c990},

doi = {10.1007/s13201-021-01522-1},

issn = {21905487},

year  = {2021},

date = {2021-01-01},

journal = {Applied Water Science},

volume = {11},

number = {12},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Groundwater plays a vital role in the sustainable development of agriculture, society and economy, and it's demand is increasing due to low rainfall, especially in arid and semiarid regions. In this context, delineation of groundwater potential zones is essential for meeting the demand of different sectors. In this research, the integrated approach consisting of analytical hierarchy process (AHP), multiple influence factors (MIF) and receiver operating characteristics (ROC) was applied. The demarcation of groundwater potential zones is based on thematic maps, namely Land Use/Land Cover (LULC), Digital Elevation Model (DEM), hillshade, soil texture, slope, groundwater depth, geomorphology, Normalized Difference Vegetation Index (NDVI), and flow direction and accumulation. The pairwise comparison matrix has been created, and weights are assigned to each thematic layer. The comparative score to every factor was calculated from the overall weight of two major and minor influences. Groundwater potential zones were classified into five classes, namely very poor, poor, moderate, good and very good, which cover an area as follows: 3.33 km2, 785.84 km2, 1147.47 km2, 595.82 km2 and 302.65 km2, respectively, based on AHP method. However, the MIF groundwater potential zones map was classified into five classes: very poor, poor, moderate, good and very good areas covered 3.049 km2, 567.42 km2, 1124.50 km2 868.86 km2 and 266.67 km2, respectively. The results of MIF and AHP techniques were validated using receiver operating characteristics (ROC). The result of this research would be helpful to prepare the sustainable groundwater planning map and policy. The proposed framework has admitted to test and could be implemented in different in various regions around the world to maintain the sustainable practices. © 2021, The Author(s).},

note = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85117754305,

title = {Grand Ethiopian Renaissance Dam and hydrologic hegemony over Abbay Basin},

author = { T.G. Andualem and M. Kassa and G. Getachew Demeke and G. Hewa and I.A. Dar and Q.B. Pham and T.J. Yamada},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117754305&doi=10.1007%2fs40899-021-00568-y&partnerID=40&md5=ef9689c8c3486900c6465c0b3260148f},

doi = {10.1007/s40899-021-00568-y},

issn = {23635037},

year  = {2021},

date = {2021-01-01},

journal = {Sustainable Water Resources Management},

volume = {7},

number = {6},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The hydropolitics over the Abbay basin has a long history of hydrologic hegemony by Egypt. The conflict over the Abbay River mainly occurred between Egypt, Ethiopia, and Sudan. Even though the river is shared by 11 countries, the use is principally dominated by Egypt in the last decades. This paper aimed to review the hydropolitics of Abbay/Blue Nile, its challenges, opportunities, and also the impacts of constructing the GERD over the riparian states (Ethiopia; Egypt; and Sudan). This review was done by searching and reviewing of papers published in reputable journals (peer reviewed and open access) focused on Abbay Basin, GERD and transboundary rivers. Egyptians said that Abbay is the primary water resource and their lifeblood and desires to continue their power on the Abbay basin. On the opposite side, Ethiopia, contributing about 86% of the flow has not been benefited from the river. This controversy has brought disagreement between Ethiopia, Egypt, and Sudan but mainly the tensions between Egypt and Ethiopia are running high. For the last 40 years, the two nations haven't experienced such resistance. However, the starting of the construction of the Grand Ethiopian Renaissance Dam (GERD) brought deviation. Ethiopians are constructing the dam over the river Abbay by their source of finance and people for ensuring their capacity and the moral issue of the people. Egypt’s hydrological hegemony on the Abbay river is reversed from the dominance of one state to the equitable and benefit-sharing of the transboundary river. Even though, the standard water program has been developed, the riparian states haven't entirely approved it due to current and projected water needs, conflict of interest, little data, and lack of mutual aid. The variety of hydrological processes within the Abbay basin makes it challenging to implement a standard plan that could be applied for overall riparian states. Although Egypt and Sudan are resisting the efforts to incorporate the other upstream riparian’s within the negotiations, they have to understand that the river and its management must be advanced from a regional viewpoint. The development of GERD within the Abbay river by Ethiopia has a positive impact on downstream countries because it provides electricity, regulates the flow, controls flood, and reduces the cost of reservoir maintenance in downstream dams. A comprehensive understanding of hydrological processes is currently essential for effective water management in the Abbay basin. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85116600794,

title = {Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin},

author = { P. Ditthakit and S. Pinthong and N. Salaeh and F. Binnui and L. Khwanchum and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116600794&doi=10.1038%2fs41598-021-99164-5&partnerID=40&md5=ec4495542e25f6e413288d13118ffa06},

doi = {10.1038/s41598-021-99164-5},

issn = {20452322},

year  = {2021},

date = {2021-01-01},

journal = {Scientific Reports},

volume = {11},

number = {1},

publisher = {Nature Research},

abstract = {Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e.; GR2M model). Two regionalization methods (i.e.; regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information. © 2021, The Author(s).},

note = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85114677703,

title = {Validation of double averaged velocity method in a variable width river},

author = { E. Shahiri Tabarestani and H. Afzalimehr and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114677703&doi=10.1007%2fs12145-021-00692-5&partnerID=40&md5=41c9ee08183e272d58bdfeb1d55e7b1c},

doi = {10.1007/s12145-021-00692-5},

issn = {18650473},

year  = {2021},

date = {2021-01-01},

journal = {Earth Science Informatics},

volume = {14},

number = {4},

pages = {2265-2278},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Comprehensive preview of flow characteristics and bedforms in rivers is one of the imperative purposes of river restoration projects. This study aimed to assess the application of double averaged technique for estimating the flow velocity and shear velocity in gravel bedforms by field research throughout the Babolroud River in north of Iran. Also, quadrant analysis carried out to investigate the turbulence structure over a pool-riffle bedforms. Based on data collected in a 95 m reach with variable width and using Acoustic Doppler Velocimeter (ADV), a double averaged velocity profile was obtained. This method validation was conducted by logarithmic law and also boundary layer characteristics method for shear velocity estimation within the bedform. The results demonstrate that the double averaged velocity profile was fitted on the logarithmic law in the inner layer and deviated from it in the outer layer, as was expected. On the other hand, the calculated shear velocity by two methods of logarithmic law and boundary layer characteristics was consistent and had small discrepancies, which indicates the double averaged method validity. The results of quadrant analysis illustrated the ejections and sweeps, as a dominant bursting event near the bed and water surface in the pool center, respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85102996743,

title = {Author Correction: Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India (Scientific Reports, (2021), 11, 1, (4470), 10.1038/s41598-021-83512-6)},

author = { T. Basu and A. Das and Q.B. Pham and N. Al-Ansari and N.T.T. Linh and G. Lagerwall},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102996743&doi=10.1038%2fs41598-021-86634-z&partnerID=40&md5=7df21dd5a0dc20745f2c83b436c6d60d},

doi = {10.1038/s41598-021-86634-z},

issn = {20452322},

year  = {2021},

date = {2021-01-01},

journal = {Scientific Reports},

volume = {11},

number = {1},

publisher = {Nature Research},

abstract = {In the original version of this Article, Nguyen Thi Thuy Linh was incorrectly affiliated with ‘Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam’ and ‘Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang 550000, Vietnam’. The correct affiliation is listed below. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam. Additionally, the corresponding email of Nguyen Thi Thuy Linh was incorrect. Correspondence and request for materials should be addressed to linhntt@tlu.edu.vn. These errors have now been corrected in the HTML and PDF versions of this Article, and in the accompanying Supplementary Information file. © 2021, The Author(s).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85101517298,

title = {Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India},

author = { T. Basu and A. Das and Q.B. Pham and N. Al-Ansari and N.T.T. Linh and G. Lagerwall},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101517298&doi=10.1038%2fs41598-021-83512-6&partnerID=40&md5=6c9ea4b4f913bdab325d92bdd44403b8},

doi = {10.1038/s41598-021-83512-6},

issn = {20452322},

year  = {2021},

date = {2021-01-01},

journal = {Scientific Reports},

volume = {11},

number = {1},

publisher = {Nature Research},

abstract = {The loss of peri-urban wetlands is a major side effect of urbanization in India in recent days. Timely and proper assessment of wetland area change is essential for the conservation of wetlands. This study follows the integrated way of the peri-urban wetland degradation assessment in the case of medium and small-size urban agglomerations with a special focus on Chatra Wetland. Analysis of land-use and land cover (LULC) maps of the past 28 years shows a decrease of 60% area of the wetland including marshy land. This has reduced the ecosystem services value by about 71.90% over the period 1991–2018. From this end, The Land Change Modeler of IDRISI TerrSet using the combination of MLPNN and Markov Chain has been used to predict the LULC map of this region. The scenario-based modeling following the LULC conversion and nine explanatory variables suggests the complete loss of this wetland by 2045. However, the authors have also tried to present a future LULC pattern of this region based on an environmental perspective. This proposed map suggests possible areas for built-up expansion on the western side of the city without significantly affecting the environment. © 2021, The Author(s).},

note = {15},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85121536099,

title = {An Integrated Approach for Delineating and Characterizing Groundwater Depletion Hotspots in a Coastal State of India},

author = { A. Basak and J. Das and A. T.M.S. Rahman and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121536099&doi=10.1007%2fs12594-021-1883-z&partnerID=40&md5=ec87232036188c1dbe123bdbbf4ada6c},

doi = {10.1007/s12594-021-1883-z},

issn = {00167622},

year  = {2021},

date = {2021-01-01},

journal = {Journal of the Geological Society of India},

volume = {97},

number = {11},

pages = {1429-1440},

publisher = {Springer},

abstract = {Visualization of present state of aquifers and identification of groundwater depletion hotspots are important tools in preparing an effective groundwater management plan. Therefore, this study developed an integrated framework by bridging a number of relevant factors to characterize and visualize groundwater depletion hotspots in Andhra Pradesh, India. Firstly, the groundwater status was assessed by detecting spatio-temporal trends in groundwater levels of 429 dug well sites from 2004 to 2018 using Mann-Kendall (MK)/modified Mann-Kendal (mMK), Spearman’s Rho test, and the magnitude of the slope was determined by Sen’s slope estimator. Subsequently, multiple decision factors were considered in the analytical hierarchy process (AHP) method for producing the groundwater stress zone map. A multicollinearity test was performed prior to the incorporation of these factors in order to improve the decision-making power of the AHP method. The results of the groundwater stress zoning map showed that 19.99%, 16.93%, 24.63%, 18.86% and 19.59 % of areas were classified as low, moderate, high and very high stress zones, respectively. Results also identified the south-western parts as groundwater depletion hotspots. Furthermore, validation results using Sen’s slope map, evaluation metrics of ROC (receiver operating characteristics) and AUC (area under curve) showed that AHP method had exhibited a reliable performance with an accuracy of 76.7%. Thus, the applied integrated approach can be used to explicitly characterize groundwater status by integrating different factors. The findings of our study also would be helpful for water resources managers and planners who need to design proper and sustainable management of groundwater resources. © 2021, Geol. Soc. India.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85118706222,

title = {Estimating baseflow and baseflow index in ungauged basins using spatial interpolation techniques: A case study of the southern river basin of Thailand},

author = { P. Ditthakit and S. Nakrod and N. Viriyanantavong and A.D. Tolche and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118706222&doi=10.3390%2fw13213113&partnerID=40&md5=e0c0cb492daed91e13776c5f3c0ae423},

doi = {10.3390/w13213113},

issn = {20734441},

year  = {2021},

date = {2021-01-01},

journal = {Water (Switzerland)},

volume = {13},

number = {21},

publisher = {MDPI},

abstract = {This research aims to estimate baseflow (BF) and baseflow index (BFI) in ungauged basins in the southern part of Thailand. Three spatial interpolation methods (namely; inverse distance weighting (IDW); kriging; and spline) were utilized and compared in regard to their performance. Two baseflow separation methods, i.e., the local minimum method (LM) and the Eckhardt filter method (EF), were investigated. Runoff data were collected from 65 runoff stations. These runoff stations were randomly selected and divided into two parts: 75% and 25% for the calibration and validation stages, respectively, with a total of 36 study cases. Four statistical indices including mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and combined accuracy (CA), were applied for the performance evaluation. The findings revealed that monthly and annual BF and BFI calculated by EF were mostly lower than those calculated by LM. Furthermore, IDW gave the best performance among the three spatial interpolation techniques by providing the highest r-value and the lowest MAE, RMSE, and CA values for both the calibration and validation stages, followed by kriging and spline, respectively. We also provided monthly and annual BF and BFI maps to benefit water resource management. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85118304932,

title = {Quantitative assessment of regional land use and climate change impact on runoff across Gilgit watershed},

author = { M. Shahid and K.U. Rahman and S. Haider and H.F. Gabriel and A.J. Khan and Q.B. Pham and C.B. Pande and N.T.T. Linh and D.T. Anh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118304932&doi=10.1007%2fs12665-021-10032-x&partnerID=40&md5=89f1ac0485d9c0ca61ea1cd9304593e7},

doi = {10.1007/s12665-021-10032-x},

issn = {18666280},

year  = {2021},

date = {2021-01-01},

journal = {Environmental Earth Sciences},

volume = {80},

number = {22},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Regional climate change (CC) and land use changes (LUCs) can significantly influence the hydrological processes at watershed scale. Different studies have investigated the impact of climate change in the Indus Basin. However, there is a need to investigate the impact of environmental changes on the regional hydrology over a complex topographic region. This study quantitatively assesses the relative contributions of CC and LUC on runoff alterations across Gilgit watershed by using multivariable calibration approach using the Soil and Water Assessment Tool (SWAT). Mann–Kendall (MK) and Pettitt tests are applied to identify the trends and changes in runoff and climatic variables during 1985–2013. The supervised classification is performed to acquire land use maps and other quantitative details required for the analyses. Moreover, Indicators of Hydrologic Alterations (IHA) analyses were performed for the first time in the Gilgit watershed to investigate the impact of CC and LUCs during the pre- and post-impact periods. The results demonstrated that precipitation, temperature, and runoff of the Gilgit watershed presented significant increasing trends. The change point using Pettitt test is depicted in 1999, 1995, and 1998, respectively. The mean annual increasing rate of precipitation, temperature, and runoff is 4.92 mm/year, 0.04 °C/year, and 2.60 m3/year, respectively. SWAT model performed well and the relative attributed contribution of CC to runoff change is 97.22% and it is 2.78% for LUC. The IHA results showed that runoff has significantly increased in post-impact (1999–2013) as compared to pre-impact (1985–1998), which was further confirmed by analyzing the IHA results using percent bias (PBIAS). Significant overestimation of runoff (higher runoff in post-impact period) was observed in the wet (maximum runoff) season. This study demonstrated that the high contribution of CC to runoff change is mainly due to the change in climate variables and global warming trends. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85110789908,

title = {Credibility of design rainfall estimates for drainage infrastructures: extent of disregard in Nigeria and proposed framework for practice},

author = { O. Aiyelokun and Q.B. Pham and O. Aiyelokun and A. Malik and S. Adarsh and B. Mohammadi and N.T.T. Linh and M. Zakwan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110789908&doi=10.1007%2fs11069-021-04889-1&partnerID=40&md5=7236519ca24b2732d0531dd874a4029b},

doi = {10.1007/s11069-021-04889-1},

issn = {0921030X},

year  = {2021},

date = {2021-01-01},

journal = {Natural Hazards},

volume = {109},

number = {2},

pages = {1557-1588},

publisher = {Springer Science and Business Media B.V.},

abstract = {Rainfall intensity or depth estimates are vital input for hydrologic and hydraulic models used in designing drainage infrastructures. Unfortunately, these estimates are susceptible to different sources of uncertainties including climate change, which could have high implications on the cost and design of hydraulic structures. This study adopts a systematic literature review to ascertain the disregard of credibility assessment of rainfall estimates in Nigeria. Thereafter, a simple framework for informing the practice of reliability check of rainfall estimates was proposed using freely available open-source tools and applied to the north central region of Nigeria. The study revealed through a synthesis matrix that in the last decade, both empirical and theoretical methods have been applied in predicting design rainfall intensities or depths for different frequencies across Nigeria, but none of the selected studies assessed the credibility of the design estimates. This study has established through the application of the proposed framework that drainage infrastructure designed in the study area using 100–1000-year return periods are more susceptible to error. And that the extent of the credibility of quantitative estimates of extreme rains leading to flooding is not equal for each variability indicator across a large spatial region. Hence, to optimize informed decision-making regarding flood risk reduction by risk assessor, variability and uncertainty of rainfall estimates should be assessed spatially to minimize erroneous deductions. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.},

note = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2-s2.0-85116743229,

title = {An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morpho-tectonic parameters},

author = { S.A. Abu El-Magd and H.O. Orabi and S.A. Ali and F. Parvin and Q.B. Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116743229&doi=10.1007%2fs12665-021-10013-0&partnerID=40&md5=05c453151e79ceb35762cebca25924f6},

doi = {10.1007/s12665-021-10013-0},

issn = {18666280},

year  = {2021},

date = {2021-01-01},

journal = {Environmental Earth Sciences},

volume = {80},

number = {20},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {An arid climate and lacking adequate flood management systems are the main reasons for flash flood events in arid and semi-arid areas. The study area is a part of the Sinai Peninsula in Egypt, Wadi Gharandal subjected to several flooded events during the past decades. The present study incorporated hydrologic indices and morpho-tectonic parameters to identify the potential sediment accumulation and erosion. Integration of hydrologic indices, stream power index, sediment transport, and topographic wetness indices with morpho-tectonic parameters to produce and detect erosion and sedimentation associated with flash floods is the main objective. Consequently, the geographic information system (GIS) and the fuzzy k-means clustering algorithm were implemented for spatial data classification and management. The applied method revealed that the hydrologic and morphometric parameters play major roles in flash flood contributing factors. The low slope areas are associated with low run-off connected with a high sediment accumulation. Conversely, a high level of erosion is encountered in the steeper slope areas. Furthermore, terrain and lithology are decisive in sediment accumulation and erosion risk in the study area. The present study demonstrates the hydrologic and morpho-tectonic parameters with remote sensing data set are efficient tools in evaluating flash floods, potential erosion, and management, supporting the urban planner for future development. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.},

note = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}