2021
Ali, S. A.; Parvin, F.; Vojteková, J.; Costache, R.; Linh, N. T. T.; Pham, Q. B.; Vojtek, M.; Gigović, L.; Ahmad, A.; Ghorbani, M. A.
GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms Journal Article
In: Geoscience Frontiers, vol. 12, no. 2, pp. 857-876, 2021, ISSN: 16749871, (45).
@article{2-s2.0-85098724608,
title = {GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms},
author = { S.A. Ali and F. Parvin and J. Vojteková and R. Costache and N.T.T. Linh and Q.B. Pham and M. Vojtek and L. Gigović and A. Ahmad and M.A. Ghorbani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098724608&doi=10.1016%2fj.gsf.2020.09.004&partnerID=40&md5=458f533ef1f353f6a81e4756e0a16707},
doi = {10.1016/j.gsf.2020.09.004},
issn = {16749871},
year = {2021},
date = {2021-01-01},
journal = {Geoscience Frontiers},
volume = {12},
number = {2},
pages = {857-876},
publisher = {Elsevier B.V.},
abstract = {Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naïve Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naïve Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%). © 2020 Elsevier B.V.},
note = {45},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naïve Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naïve Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%). © 2020 Elsevier B.V.
Ali, S. A.; Parvin, F.; Al-Ansari, N.; Pham, Q. B.; Ahmad, A.; Meena, S. R.; Anh, D. T.; Ba, L. H.; Thai, V. N.
Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS: a case study of Memari Municipality, India Journal Article
In: Environmental Science and Pollution Research, vol. 28, no. 6, pp. 7528-7550, 2021, ISSN: 09441344, (25).
@article{2-s2.0-85092311010,
title = {Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS: a case study of Memari Municipality, India},
author = { S.A. Ali and F. Parvin and N. Al-Ansari and Q.B. Pham and A. Ahmad and S.R. Meena and D.T. Anh and L.H. Ba and V.N. Thai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092311010&doi=10.1007%2fs11356-020-11004-7&partnerID=40&md5=2347aaf0d78b5c972e6f946e00b4a3a2},
doi = {10.1007/s11356-020-11004-7},
issn = {09441344},
year = {2021},
date = {2021-01-01},
journal = {Environmental Science and Pollution Research},
volume = {28},
number = {6},
pages = {7528-7550},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert’s views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.},
note = {25},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert’s views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
2020
Ali, S. A.; Parvin, F.; Pham, Q. B.; Vojtek, M.; Vojteková, J.; Costache, R.; Linh, N. T. T.; Nguyen, H. Q.; Ahmad, A.; Ghorbani, M. A.
In: Ecological Indicators, vol. 117, 2020, ISSN: 1470160X, (82).
@article{2-s2.0-85086627928,
title = {GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia},
author = { S.A. Ali and F. Parvin and Q.B. Pham and M. Vojtek and J. Vojteková and R. Costache and N.T.T. Linh and H.Q. Nguyen and A. Ahmad and M.A. Ghorbani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086627928&doi=10.1016%2fj.ecolind.2020.106620&partnerID=40&md5=79d4cf47c90f0fa953cf6a8125f35802},
doi = {10.1016/j.ecolind.2020.106620},
issn = {1470160X},
year = {2020},
date = {2020-01-01},
journal = {Ecological Indicators},
volume = {117},
publisher = {Elsevier B.V.},
abstract = {Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying the susceptible areas to flood is an important task for every country to prevent such dangerous consequences. The present study developed a framework for identifying flood-prone areas of the Topľa river basin, Slovakia using geographic information system (GIS), multi-criteria decision making approach (MCDMA), bivariate statistics (Frequency Ratio (FR); Statistical Index (SI)) and machine learning (Naïve Bayes Tree (NBT); Logistic Regression (LR)). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. To access the relationship and interdependences among the criteria, decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) were used. Based on the experts’ decisions, the DEMATEL-ANP model was used to compute the relative weights of different criteria and a GIS-based linear combination was performed to derive the susceptibility index. Separately, the flood susceptibility index computation through NBT-FR and NBT-SI hybrid models assumed, in the first stage, the estimation of the weight of each class/category of conditioning factor through SI and FR and the integration of these values in NBT algorithm. The application of LR stand-alone required the calculation of the weights of conditioning factors by analysing their spatial relation with the location of the historical flood events. The study revealed that very high and high flood susceptibility classes covered between 20% and 47% of the study area, respectively. The validation of results, using the past flood points, highlighted that the hybrid DEMATEL-ANP model was the most performant with an Area Under ROC curve higher than 0.97, an accuracy of 0.922 and a value of HSS of 0.844. The presented methodological approach used for the identification of flood susceptible areas can serve as an alternative for the updating of preliminary flood risk assessment based on the EU Floods Directive. © 2020 Elsevier Ltd},
note = {82},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying the susceptible areas to flood is an important task for every country to prevent such dangerous consequences. The present study developed a framework for identifying flood-prone areas of the Topľa river basin, Slovakia using geographic information system (GIS), multi-criteria decision making approach (MCDMA), bivariate statistics (Frequency Ratio (FR); Statistical Index (SI)) and machine learning (Naïve Bayes Tree (NBT); Logistic Regression (LR)). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. To access the relationship and interdependences among the criteria, decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) were used. Based on the experts’ decisions, the DEMATEL-ANP model was used to compute the relative weights of different criteria and a GIS-based linear combination was performed to derive the susceptibility index. Separately, the flood susceptibility index computation through NBT-FR and NBT-SI hybrid models assumed, in the first stage, the estimation of the weight of each class/category of conditioning factor through SI and FR and the integration of these values in NBT algorithm. The application of LR stand-alone required the calculation of the weights of conditioning factors by analysing their spatial relation with the location of the historical flood events. The study revealed that very high and high flood susceptibility classes covered between 20% and 47% of the study area, respectively. The validation of results, using the past flood points, highlighted that the hybrid DEMATEL-ANP model was the most performant with an Area Under ROC curve higher than 0.97, an accuracy of 0.922 and a value of HSS of 0.844. The presented methodological approach used for the identification of flood susceptible areas can serve as an alternative for the updating of preliminary flood risk assessment based on the EU Floods Directive. © 2020 Elsevier Ltd
2016
Ahmad, A.; Parveen, S.; Brożek, J.; Dey, D.
Antennal sensilla of phytophagous and predatory pentatomids (Hemiptera: Pentatomidae): A comparative study of four genera Journal Article
In: Zoologischer Anzeiger, vol. 261, pp. 48-55, 2016, ISSN: 00445231, (20).
@article{2-s2.0-84962269307,
title = {Antennal sensilla of phytophagous and predatory pentatomids (Hemiptera: Pentatomidae): A comparative study of four genera},
author = { A. Ahmad and S. Parveen and J. Brożek and D. Dey},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962269307&doi=10.1016%2fj.jcz.2016.03.007&partnerID=40&md5=441579e8b6c627be73e1f99c13b29984},
doi = {10.1016/j.jcz.2016.03.007},
issn = {00445231},
year = {2016},
date = {2016-01-01},
journal = {Zoologischer Anzeiger},
volume = {261},
pages = {48-55},
publisher = {Elsevier GmbH},
abstract = {Antennal sensilla of four genera of Pentatomidae (Hemiptera: Heteroptera) belonging to two subfamilies viz., Pentatominae and Asopinae were examined using scanning electron microscopy. The representatives selected from the subfamily Pentatominae (phytophagous)-Dolycoris indicus (Stål) and Plautia crossota (Dallas), and from the subfamily Asopinae (predatory)-Perillus bioculatus (Fabricius) and Eocanthecona furcellata (Wolff). They were studied in order to morphologically differentiate the sensilla present on the antennae. This study indicated the presence of six types of sensilla, most of which were found on the flagellar segments rather than on the scape or pedicel. Sensilla campaniformia were recorded on the flagellar segments as well as the scape and pedicel. Phytophagous species (D. indicus and P. crossota) showed a higher number of sensilla trichodea, basiconica and chaetica whereas sensilla coeloconica were restricted to the predatory species studied (E. furcellata and P. bioculatus). These differences may be related to their evolutionary history. The presence of multiporous chemo-sensilla as basiconic sensilla type and sensilla placoidea reflect the ability of the antennae to perceive chemical stimuli. A relatively higher abundance of mechanoreceptors could be considered as potential detectors for various mechanical functions and these appeared to be correlated with the genus and type of feeding but not with the sex of the studied species. © 2016 Elsevier GmbH.},
note = {20},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Antennal sensilla of four genera of Pentatomidae (Hemiptera: Heteroptera) belonging to two subfamilies viz., Pentatominae and Asopinae were examined using scanning electron microscopy. The representatives selected from the subfamily Pentatominae (phytophagous)-Dolycoris indicus (Stål) and Plautia crossota (Dallas), and from the subfamily Asopinae (predatory)-Perillus bioculatus (Fabricius) and Eocanthecona furcellata (Wolff). They were studied in order to morphologically differentiate the sensilla present on the antennae. This study indicated the presence of six types of sensilla, most of which were found on the flagellar segments rather than on the scape or pedicel. Sensilla campaniformia were recorded on the flagellar segments as well as the scape and pedicel. Phytophagous species (D. indicus and P. crossota) showed a higher number of sensilla trichodea, basiconica and chaetica whereas sensilla coeloconica were restricted to the predatory species studied (E. furcellata and P. bioculatus). These differences may be related to their evolutionary history. The presence of multiporous chemo-sensilla as basiconic sensilla type and sensilla placoidea reflect the ability of the antennae to perceive chemical stimuli. A relatively higher abundance of mechanoreceptors could be considered as potential detectors for various mechanical functions and these appeared to be correlated with the genus and type of feeding but not with the sex of the studied species. © 2016 Elsevier GmbH.