Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

作     者：Guangzhi Rong Kaiwei Li Zhijun Tong Xingpeng Liu Jiquan Zhang Yichen Zhang Tiantao Li Guangzhi Rong;Kaiwei Li;Zhijun Tong;Xingpeng Liu;Jiquan Zhang;Yichen Zhang;Tiantao Li

作者机构：School of EnvironmentNortheast Normal UniversityChangchun 130024China Key Laboratory for Vegetation EcologyMinistry of EducationChangchun 130117China State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation RestorationNortheast Normal UniversityChangchun 130117China School of Emergency ManagementChangchun Institute of TechnologyChangchun 130012China Chengdu University of TechnologyChengdu 610059China State Key Laboratory of Geohazard Prevention and Geo Environment ProtectionChengdu University of TechnologyChengdu 610059China 

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

年 卷 期：2023年第14卷第3期

页      面：163-179页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081803[工学-地质工程] 081104[工学-模式识别与智能系统] 08[工学] 0818[工学-地质资源与地质工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by“The National Key Research and Development Program of China(2018YFC1508804) The Key Scientific and Technology Program of Jilin Province(20170204035SF) The Key Scientific and Technology Research and Development Program of Jilin Province(20200403074SF) The Key Scientific and Technology Research and Development Program of Jilin Province(20180201035SF) National Natural Science Fund for Young Scholars of China(41907238)” 

主　　题：Landslide population amount risk assessment Integrated Machine Learning Extreme precipitation scenarios Future socioeconomic development scenarios 

摘      要：In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,***,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide *** the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in *** results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility *** distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard *** high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk *** LPAR increased with the intensity of extreme *** LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)scenario and the highest in the“regional rivalry(SSP3)*** summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive *** results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landsli