基于信息量与机器学习耦合模型的滑坡易发性评价对比分析OA北大核心CSTPCD

The geological environment of Zhen'an County,Shangluo City,Shaanxi Province is complex,and landslide disasters occur frequently.This study focused on Zhen'an County and proposed combi-ning the Information Value Model(IVM)with four machine learning methods(Support Vector Ma-chine,Radial Basis Function,Extreme Learning Machine,and Back Propagation Neural Network)to construct coupled models for comparative landslide susceptibility assessment.First,based on geological disaster survey data of the county,nine influencing factors were selected from aspects such as topogra-phy,geological environment,meteorology,hydrology,and human engineering activities.The Pearson correlation coefficient was used to analyze the correlations between these factors,forming a landslide susceptibility evaluation index system.Secondly,the Information Value Model was used to quantify these factors,with the information value of each factor serving as input data for the machine learning models.Landslide susceptibility assessments were then conducted using the four machine learning mod-els(SVM,RBF,ELM,and BPNN),and accuracy was verified through Receiver Operating Characteris-tic(ROC)curves.The results indicate that:Landslides in the study area are mainly distributed along roadsides,riverbanks,and fractured mountain zones with poor geological conditions.Human engineering activities,meteorology,and geological structures are the main factors influencing landslide development in the area.Among the four coupling models,the IV-BPNN model is more suitable for evaluating the landslide susceptibility assessment in the study area.This model shows the number of landslide points distributed per unit area in extremely high and high-risk areas is more concentrated,with 85.15%of landslide disaster points distributed in only 21.43%of the area,outperforming the other coupling mod-els.The AUC values for the IV-SVM,IV-RBF,IV-ELM,and IV-BPNN models are 0.841,0.813,0.838,and 0.863,respectively.Among them,the IV-BPNN model has the highest accuracy,with an in-crease of 19.5%compared to the AUC value of the information model(0.722),indicating higher relia-bility.This model effectively addresses the issues of non-uniform dimensions of influencing factors and complex nonlinear relationships between factors that are common in traditional landslide susceptibility assessment methods.It can more accurately identify high-risk areas for potential landslide disasters,providing reference for local landslide disaster prevention and control efforts.