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

Di xue qian yuan., 2021-03, Vol.12 (2), p.857-876 [Peer Reviewed Journal]

2021 Elsevier B.V. ;Copyright Elsevier Science Ltd. Mar 2021 ;Copyright © Wanfang Data Co. Ltd. All Rights Reserved. ;ISSN: 1674-9871 ;EISSN: 2588-9192 ;DOI: 10.1016/j.gsf.2020.09.004

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  • Title:
    GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
  • Author: 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
  • 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
  • Is Part Of: Di xue qian yuan., 2021-03, Vol.12 (2), p.857-876
  • Description: 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.
  • Publisher: Oxford: Elsevier B.V
  • Language: English
  • Identifier: ISSN: 1674-9871
    EISSN: 2588-9192
    DOI: 10.1016/j.gsf.2020.09.004
  • Source: DOAJ Directory of Open Access Journals
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