冯显杰, 李益敏, 邓选伦,等.高山峡谷地区地质灾害易发性评价——以怒江州为例[J].河南理工大学学报（自然科学版）,2024,43(3):70-80.

FENG X J, LI Y M, DENG X L, et al.Susceptibility evaluation of geological disasters in the alpine and canyon areas：A case study of Nujiang Prefecture[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(3):70-80.

高山峡谷地区地质灾害易发性评价——以怒江州为例

冯显杰1, 李益敏2,3, 邓选伦2, 赵娟珍1, 杨一铭2

1.云南大学 国际河流与生态安全研究院，云南 昆明  650500 2.云南大学 地球科学学院，云南 昆明  650500 3.云南省高校国产高分卫星遥感地质工程研究中心，云南 昆明  650500

摘要:高山峡谷地区地质灾害频发, 目的 为探究地质灾害易发性空间分布状况， 方法 以怒江州为研究区，综合地质条件、气象水文、植被覆盖等因素，筛选高程、坡度、坡向、曲率、起伏度等12个共线性低的评价因子，构建区域易发性评价指标体系，并基于栅格单元采用信息量（information value，IV）模型、信息量-BP神经网络（information value-back propagation neural networks，IV-BPNN）耦合模型和信息量-支持向量机（information value-support vector machine，IV-SVM）耦合模型进行地质灾害易发性评价。 结果 结果表明：（1）用实际地质灾害点验证易发性结果，灾害点与3种易发性结果在空间分布上具有较好的一致性；（2）将易发性指数划分为低、中、高和极高易发4个等级，其中IV模型、IV-BPNN耦合模型与IV-SVM耦合模型的高+极高易发区面积占比分别为37.12％，32.36％，23.08％，高与极高易发区呈线状分布，主要集中在怒江、澜沧江、独龙江等水系沿岸地区、道路附近和地质构造活跃的区域；（3）IV模型、IV-BPNN耦合模型与IV-SVM耦合模型的受试者曲线（receiver operating characteristic curve，ROC）的曲线下面积（area under the curve，AUC）分别为0.884，0.889，0.901。  结论 种地质灾害易发性评价模型均有较高的预测精度，其中IV-SVM耦合模型准确率最高，分区结果较可靠，可为当地政府制定地质灾害防治措施提供参考。

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

doi:10.16186/j.cnki.1673-9787.2022040062

基金项目:国家自然科学基金资助项目（41161070）；云南省科技厅-云南大学联合基金重点资助项目（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）；云南大学第二届专业学位研究生实践创新项目（ZC-22222175）

收稿日期:2022/04/22

修回日期:2022/06/18

出版日期:2024/05/15

Susceptibility evaluation of geological disasters in the alpine and canyon areas：A case study of Nujiang Prefecture

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: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 Prefecture 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，aspect，curvature，relief，etc.，were selected to construct a regional 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 susceptibility results are validated by using actual geological disaster points，showing good spatial distribution consistency between disaster points and the three susceptibility results.（2）The susceptibility index is categorized 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 geological 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 local governments in formulating measures for geological disaster prevention and control.

Key words:alpine and canyon areas;geological disasters;information model;BP neural network;support vector machine;susceptibility evaluation;Nujiang Prefecture