| GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms |
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| 作者姓名： | Sk Ajim Ali Farhana Parvin Jana Vojteková Romulus Costache Nguyen Thi Thuy Linh Quoc Bao Pham Matej Vojtek Ljubomir Gigovi? Ateeque Ahmad Mohammad Ali Ghorbani |
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| 作者单位： | Department of Geography;Department of Geography and Regional Development;Research Institute of the University of Bucharest;National Institute of Hydrology and Water Management;Thuyloi University;Institute of Research and Development;Faculty of Environmental and Chemical Engineering;Department of Geography;Sustainable Management of Natural Resources and Environment Research Group |
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| 摘 要： | 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 theworld.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),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith 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 methodwas 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,Na?ve 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%).
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| 关 键 词： | Landslide susceptibility modeling Geographic information system Fuzzy DEMATEL Analytic network process Na?ve Bayes classifier Random forest classifier |
| 收稿时间： | 6 May 2020 |
GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms |
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| Sk Ajim Ali,Farhana Parvin,Jana Vojteková,Romulus Costache,Nguyen Thi Thuy Linh,Quoc Bao Pham,Matej Vojtek,Ljubomir Gigovi?,Ateeque Ahmad,Mohammad Ali Ghorbani.GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms[J].Geoscience Frontiers,2021,12(2):857-876. |
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| Authors: | Sk Ajim Ali Farhana Parvin Jana Vojteková Romulus Costache Nguyen Thi Thuy Linh Quoc Bao Pham Matej Vojtek Ljubomir Gigovi? Ateeque Ahmad Mohammad Ali Ghorbani |
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| Institution: | Department of Geography,Faculty of Science,Aligarh Muslim University (AMU),Aligarh,UP 202002,India;Department of Geography and Regional Development,Faculty of Natural Sciences,Constantine the Philosopher University in Nitra,Trieda A.Hlinku 1,94901 Nitra,Slovakia;Research Institute of the University of Bucharest,90-92 Sos.Panduri,5th District,Bucharest 050663,Romania;National Institute of Hydrology and Water Management,Bucure?ti-Ploie?ti Road,97E,1st District,Bucharest 013686,Romania;Thuyloi University,175 Tay Son,Dong Da,Hanoi,Viet Nam;Institute of Research and Development,Duy Tan University,Danang 550000,Viet Nam;Faculty of Environmental and Chemical Engineering,Duy Tan University,Danang 550000,Viet Nam;Department of Geography,University of Defence,11000 Belgrade,Serbia;Sustainable Management of Natural Resources and Environment Research Group,Faculty of Environment and Labour Safety,Ton Duc Thang University,Ho Chi Minh City,Viet Nam |
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| 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%). |
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