A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province,Türkiye

作     者：Gizem Karakas Sultan Kocaman Candan Gokceoglu Gizem Karakas;Sultan Kocaman;Candan Gokceoglu

作者机构：Graduate School of Science and EngineeringHacettepe University06800BeytepeAnkaraTurkey Department of Geomatics EngineeringHacettepe University06800BeytepeAnkaraTurkey Department of Geological EngineeringHacettepe University06800BeytepeAnkaraTurkey 

出 版 物：《International Journal of Disaster Risk Science》 (国际灾害风险科学学报（英文版）)

年 卷 期：2023年第14卷第2期

页      面：326-341页

核心收录：

学科分类：083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 

基　　金：University of Miami Florida International University 

主　　题：Earthquakes Floods Fuzzy inference systems Landslides Multi-hazard susceptibility assessment Random forest Türkiye 

摘      要：Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural *** this study,we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide,flood,and earthquake hazard assessments for a basin in Elazig Province,Tü*** produce the landslide susceptibility map,an ensemble machine learning algorithm,random forest,was chosen because of its known performance in similar *** modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the *** seismic hazard was assessed using ground motion parameters based on Arias intensity *** univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by *** results show that the random forest provided an overall accuracy of 92.3%for landslide susceptibility *** the study area,41.24%were found prone to multi-hazards(probability value50%),but the southern parts of the study area are more *** proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.