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基于最大熵模型的中尼交通廊道滑坡易发性分析
引用本文:万洋,郭捷,马凤山,刘佳,宋烨炜.基于最大熵模型的中尼交通廊道滑坡易发性分析[J].中国地质灾害与防治学报,2022,33(2):88-95.
作者姓名:万洋  郭捷  马凤山  刘佳  宋烨炜
作者单位:1.中国科学院地质与地球物理研究所/中国科学院页岩气与地质工程重点实验室, 北京 100029
基金项目:第二次青藏高原综合科学考察研究专题(2019QZKK0904);中国科学院国际合作局国际伙伴计划项目(131551KYSB20180042)
摘    要:中尼交通廊道作为中国近年来建设的重点区域,地质灾害频发,尤其是滑坡灾害层出不穷。文章基于对G216国道沿线地质灾害的实地调查以及遥感解译结果,以最大熵模型为方法,利用169个灾害点数据和8个评价因子图层预测了研究区滑坡灾害的易发性分布。根据占比划分五级风险区。结果表明,滑坡易发概率以G216为中心向外辐射逐渐降低。同时采用刀切法检验评价因子对预测结果的贡献度,确定了滑坡主导因素及其阈值。最后通过ROC曲线验证了模型的可靠性。为中尼边境公路区域建设提供一种地质灾害预测分析模型,也为青藏地区公路边坡防灾减灾提供有效支撑。

关 键 词:滑坡易发性    最大熵模型    中尼交通廊道    GIS    ROC
收稿时间:2021-11-29

Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor
Affiliation:1.Key Laboratory of Shale Gas and Geoengineering/Institute of Geology and Geophysics,Chinese Academy of Sciences, Beijing 100029, China2.Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China3.University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:As a key area of China's construction in recent years, the Sino Nepal traffic corridor has complex geological conditions and frequent geological disasters, especially landslides are the most serious. Through the field survey and remote sensing interpretation along G216 highway, we obtained the data of 169 disaster points. Using the MaxEnt model and 8 evaluation factor layers, we predicted the distribution of landslide susceptibility in the study area. We divided the results into five categories: extremely low, low, medium, high and extremely high prone areas, and their proportions are 11.48%, 41.28%, 25.21%, 10.87%, 11.16%. The probability of landslide occurrence is higher near the road and lower the farther away from the road. In addition, we used the jackknife to test the contribution of evaluation factors to the prediction results, and determine the dominant factors. The study provides a high-accuracy analysis model for the prediction of geological disasters in the China Nepal border highway area, and also provides effective support for highway slope disaster prevention in Qinghai Tibet region.
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