Risk Assessment of Slope Collapse Hazards in Mountainous Areas of Eastern Guangdong Based on SMOTETomek and Machine Learning
LI Jinxiang
LIAO Jiale
SU Wenji
Abstract：To assess the geological disaster hazard of slope collapse in mountainous areas of eastern Guangdong,this study selected Meixi Town as the representative research area.An evaluation index system was constructed com-prising the following factors:slope,aspect,plan curvature,profile curvature,stratigraphic lithology,distance from fault,distance from water system,normalized difference vegetation index(NDVI),distance from building,distance from road,and land use types.Then,a susceptibility evaluation was conducted by SMOTETomek hybrid sampling integrated with machine learning and deep learning models,including random forest(RF),gradient boosting decision tree(GBDT),3D convolutional neural network(3D CNN),and graph attention network(GAT).Based on the optimal model,precipitation analysis was incorporated to complete the risk assessment.The results show that models based on SMOTETomek hybrid sampling generally achieve higher accuracy,and the SMOTE-Tomek-GAT model has the highest precision,with an AUC value of 0.93.
Keywords：Geological disasterRisk assessmentSMOTETomekMachine learningDeep learning
Publication Date：2025-06-30
Online Publishing Date：2025-08-25(First online date of this platform, not the publication date of the document)
Pages：7( 50-56 )