Physics-informed optimization for a data-driven approach in landslide susceptibility evaluation  

作　　者：Songlin Liu Luqi Wang Wengang Zhang Weixin Sun Yunhao Wang Jianping Liu 

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]Chongqing Mingfeng Construction Engineering Co.,Ltd,Chongqing,405800,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2024年第8期3192-3205,共14页岩石力学与岩土工程学报（英文版）

基　　金：funded by the Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01);High-end Foreign Expert Introduction program(Grant No.G2022165004L);Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001).

摘　　要：Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.

关 键 词：Physics-informed Machine learning Bedrock depth Scoops 3D Landslide susceptibility 

分 类 号：P642.22[天文地球—工程地质学]