doi:10.16186/j.cnki.1673-9787.2022040062

Received:2022-04-22

Revised:2022-06-18

Online Date: 2022-12-22

Evaluation of the susceptibility of geological disasters in the alpine and canyonareas：Taking Nujiang Prefecture as an example（Online）

FENG Xianjie1, LI Yimin2,3, DENG Xuanlun2, ZHAO Juanzhen1, YANG Yiming2

1.Institute of International Rivers and Eco-Security， Yunnan University， Kunming  650500， Yunnan， China；2.School of Earth Sciences， Yunnan University， Kunming  650500， Yunnan， China；3.Research Center of Domestic High-Resolution Satellite Remote Sensing Geological Engineering， Kunming  650500， Yunnan， China

Abstract:To investigate the spatial distribution of geological hazard susceptibility in the alpine and canyon areas， we select Nujiang Prefecture as the research area. Meanwhile， 12 evaluation factors such as elevation， slope， aspect， curvature， undulation， etc.， are used to build a regional susceptible evaluation index system. Then， a total of three models， the information value （IV） model， information value-back propagation neural networks （IV-BPNN） coupled model， and information value-support vector machine （IV-SVM） coupled model， are applied to evaluate the susceptibility of geological hazards. The results of the study show that the susceptibility results have better space distribution consistency with the actual geological disaster points； the high and extremely high susceptibility areas are linearly distributed and mainly concentrate in the areas on both sides of rivers such as the Nujiang， Lancang and Dulong rivers, as well as near roads and areas with active geological formations. In addition, the areas under thecurve (AUC) of the receiver operating characteristic curve (ROC) of the IV model, IV-BPNN model and IV-SVM model are 0.884, 0.889, and 0.901, respectively, which indicates that the accuracy of IV-SVM model is higher. Theresults of the study can provide a reference for the local government to develop geological disaster prevention andcontrol measures.

Key words:geological hazard;information model;BP neural network;support vector machine;susceptibility evaluation;Nujiang Prefecture

CLC:P694