河南理工大学学报（自然科学版）2024，Vol.43Issue(3)：70-80,11.DOI:10.16186/j.cnki.1673-9787.2022040062
高山峡谷地区地质灾害易发性评价
Susceptibility evaluation of geological disasters in the alpine and canyon areas:A case study of Nujiang Prefecture
摘要
Abstract
Geological disasters are frequent in alpine and canyon areas.Objectives The aim of this study is to investigate the spatial distribution of geological disaster susceptibility.Methods It selected Nujiang Pre-fecture as the research area.By considering geological conditions,meteorological hydrology,vegetation cover,and other factors,a total of 12 evaluation factors with low collinearity,including elevation,slope,as-pect,curvature,relief,etc.,were selected to construct a regional geological hazard susceptibility evaluation index system.Then,the susceptibility of geological disasters was evaluated at grid units using 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.Results(1)The suscepti-bility results are validated by using actual geological disaster points,showing good spatial distribution con-sistency between disaster points and the three susceptibility results.(2)The susceptibility index is catego-rized into four levels:low,moderate,high,and extremely high susceptibility areas.The corresponding area proportions of high and extremely high susceptibility classes for the IV model,IV-BPNN model,and IV-SVM model are 37.12%,32.36%,and 23.08%,respectively.High and extremely high susceptibility areas exhibit a linear distribution and are mainly concentrated along coastal watercourses such as the Nujiang River,Lancang River,and Dulong River,near roads,and in areas with active geological structures.(3)The areas under the curve(AUC)of the receiver operating characteristic(ROC)curves for the IV model,IV-BPNN model,and IV-SVM model are 0.884,0.889,and 0.901,respectively.Conclusions All three geologi-cal disasters susceptibility evaluation models demonstrate high prediction accuracy,among which the IV-SVM model shows the highest accuracy and reliable zoning results,which provide valuable reference for lo-cal governments in formulating measures for geological disaster prevention and control.关键词
高山峡谷地区/地质灾害/信息量模型/BP神经网络/支持向量机/易发性评价/怒江州Key words
alpine and canyon areas/geological disasters/information model/BP neural network/support vec-tor machine/susceptibility evaluation/Nujiang Prefecture分类
天文与地球科学引用本文复制引用
冯显杰,李益敏,邓选伦,赵娟珍,杨一铭..高山峡谷地区地质灾害易发性评价[J].河南理工大学学报（自然科学版）,2024,43(3):70-80,11.基金项目
国家自然科学基金资助项目(41161070) （41161070）
云南省科技厅-云南大学联合基金重点资助项目(2019FY003017) （2019FY003017）
云南大学大湄公河次区域气候变化研究省创新团队项目(2019HC027) （2019HC027）
云南大学第二届专业学位研究生实践创新项目(ZC-22222175) （ZC-22222175）
