Landslide susceptibility mapping of mountain roads based on machine learning combined model  

作　　者：DOU Hong-qiang HUANG Si-yi JIAN Wen-bin WANG Hao 

机构地区：[1]Zijin School of Geology and Mining,Fuzhou University,Fuzhou 350116,China [2]Fujian Key Laboratory of Geohazard Prevention,Fuzhou 350002,China

出　　处：《Journal of Mountain Science》2023年第5期1232-1248,共17页山地科学学报（英文）

基　　金：the financial support from the National Natural Science Foundation of China(No.U2005205,No.42007235,No.41972268);the Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau(No.2021-P-032)。

摘　　要：Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.

关 键 词：Landslide susceptibility mapping Factor analysis MACHINELEARNING Combinedmodel Mountain roads 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] U418.55[自动化与计算机技术—控制科学与工程]