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A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir area

查看全文 作　　者：Songlin [1]Liu;Luqi [1,2,3]Wang;Wengang [1,2,3,4]Zhang;Weixin [1]Sun;Jie [5]Fu;Ting [6]Xiao;Zhenwei [7]Dai 高影响力作者 机构地区：[1]School of Civil Engineering,Chongqing University,Chongqing 400045,China;[2]Key Laboratory of New Technology for Construction of Cities in Mountain Area,Chongqing University,Ministry of Education,Chongqing 400045,China;[3]National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas,Chongqing University,Chongqing 400045,China;[4]Chongqing Field Scientific Observation Station for Landslide Hazards in Three Gorges Reservoir Area,Chongqing University,Chongqing 400045,China;[5]Center for Hydrogeology and Environmental Geology,CGS,Baoding Hebei 071051,China;[6]Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring,Ministry of Education,School of Geosciences and Info-Physics,Central South University,Changsha 410083,China;[7]Central South China Innovation Center for Geosciences,Wuhan Centre of China Geological Survey,Wuhan 430205,China高影响力机构 出　　处：《Geoscience Frontiers》索引2023年第14卷第5期,共16页高影响力期刊 基　　金：funded by the National Key R&D Program of China(Project No.2019YFC1509605);High-end Foreign Expert Introduction program(No.G20200022005 and DL2021165001L)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001)。 摘　　要：Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capability in dealing with complex nonlinear problems.However,a significant proportion of these models have neglected qualitative aspects during analysis,resulting in a lack of interpretability throughout the process and causing inaccuracies in the negative sample extraction.In this study,Scoops 3D was employed as a physics-informed tool to qualitatively assess slope stability in the study area(the Hubei Province section of the Three Gorges Reservoir Area).The non-landslide samples were extracted based on the calculated factor of safety(FS).Subsequently,the random forest algorithm was employed for data-driven landslide susceptibility analysis,with the area under the receiver operating characteristic curve(AUC)serving as the model evaluation index.Compared to the benchmark model(i.e.,the standard method of utilizing the pure random forest algorithm),the proposed method’s AUC value improved by 20.1%,validating the effectiveness of the dual-driven method(physics-informed data-driven). 关 键 词：Machine Learning Physics-informed Negative sample extraction INTERPRETABILITY Dual-driven
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