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

• 来源期刊: 地学前缘（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: Research Paper
• 页码范围: 857-876
• 页数: 20页
• 语种: 英文

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 the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibil-ity 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 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 fac-tors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was con-sidered 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 arith-metic evaluation were used for validating and comparing the results and models. The results revealed that ran-dom 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％).