Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

作     者：Ahmed Mohamed Youssef Hamid Reza Pourghasemi Ahmed Mohamed Youssef;Hamid Reza Pourghasemi

作者机构：Geology DepartmentFaculty of ScienceSohag UniversityEgypt Geological Hazards DepartmentApplied Geology SectorSaudi Geological SurveyP.O.Box 54141Jeddah21514Saudi Arabia Department of Natural Resources and Environmental EngineeringCollege of AgricultureShiraz UniversityShirazIran 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2021年第12卷第2期

页      面：639-655页

核心收录：

学科分类：081803[工学-地质工程] 08[工学] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 0704[理学-天文学] 

基　　金：Shiraz University(Grant No.98GRC1M271143) 

主　　题：Landslide susceptibility Machine learning algorithms Variables importance Saudi Arabia 

摘      要：The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their *** machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important *** study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi ***,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data *** the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating *** landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and *** area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs *** results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for *** findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other *** outcome of this study and the landslide susceptibility maps would be useful for environmental protection.