GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

• Published in: Di xue qian yuan. Vol. 12; no. 2; pp. 857 - 876
• Main Authors: Ali, Sk Ajim, Parvin, Farhana, Vojteková, Jana, Costache, Romulus, Linh, Nguyen Thi Thuy, Pham, Quoc Bao, Vojtek, Matej, Gigović, Ljubomir, Ahmad, Ateeque, Ghorbani, Mohammad Ali
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.03.2021; Elsevier Science Ltd; 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; 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
• Subjects: Algorithms; Analytic network process; Classifiers; Decision analysis; Decision making; Decision trees; Evaluation; Fuzzy DEMATEL; Geographic information system; Hydrology; Land cover; Landslide susceptibility modeling; Landslides; Machine learning; Man made disasters; Multiple criterion; Natural resources; Naïve Bayes classifier; Random forest classifier; River basins; Root-mean-square errors; Fuzzy DEMATEL; Naïve Bayes classifier; Analytic network process; Landslide susceptibility modeling; Geographic information system; Random forest classifier; Na?ve Bayes classifier
• ISSN: 1674-9871; 2588-9192
• DOI: 10.1016/j.gsf.2020.09.004

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%). [Display omitted] •Landslide susceptibility modeling plays a significant role in disaster prevention.•Three models (FDEMATEL-ANP, NBC, and RFC) were applied and compared.•The ROC curve was used for evaluating and comparing the performance of the results.•Random forest classifier (RFC) produced the best result for susceptibility assessment.•An accurate model of susceptibility is helpful for landslide risk mitigation and planning.