测绘通报 ›› 2021, Vol. 0 ›› Issue (4): 13-16.doi: 10.13474/j.cnki.11-2246.2021.0103

• 生态环境动态监测 • 上一篇    下一篇

金沙江流域滑坡易发性空间预报分析

苏美臣1, 魏晓燕2, 周峻松2, 汪祎勤2   

  1. 1. 云南师范大学, 云南 昆明 650500;
    2. 云南省测绘资料档案馆, 云南 昆明 650034
  • 收稿日期:2021-01-11 修回日期:2021-02-22 出版日期:2021-04-25 发布日期:2021-04-30
  • 通讯作者: 魏晓燕。E-mail:19423221@qq.com
  • 作者简介:苏美臣(1995-),女,硕士生,研究方向为空间统计分析。E-mail:458025406@qq.com
  • 基金资助:
    国家自然科学基金(42061074;41701470)

Analysis and prediction of landslide susceptibility in Jinsha River

SU Meichen1, WEI Xiaoyan2, ZHOU Junsong2, WANG Yiqin2   

  1. 1. Yunnan Normal University, Kunming 650500, China;
    2. Yunnan Provincial Archives of Surveying and Mapping, Kunming 650034, China
  • Received:2021-01-11 Revised:2021-02-22 Online:2021-04-25 Published:2021-04-30

摘要: 灾害易发性预报是提高灾害防控能力的第一步。针对位于云南省内的金沙江流域因地势险峻、生态环境脆弱,加之近年来人为活动增多已成为地质灾害高发区的现状,本文以金沙江德钦至华坪段滑坡灾害为例,运用Maxent和随机森林两种机器学习模型对滑坡空间分布作归因与预测,并对两者之间的差异进行对比分析。试验结果表明,随机森林模型的预测精度高于Maxent模型,AUC值为0.72。

关键词: 滑坡预报, 随机森林, Maxent模型, 金沙江流域, 易发性制图

Abstract: Disaster susceptibility mapping is the first step to improve the ability of control and prediction. In view of the problem that Jinsha River located in Yunnan province due to steep terrain and fragile ecological environment, combined with the increase of human activities in recent year, its basin has become the most frequent incident area of geological disaster in china. Taking Jinsha River of Deqin-Huapingas an example, this paper uses two methods(maxent and random forest model) for attribution and prediction of spatial distribution of landslides, and compares their differences. The result show that the prediction accuracy of random forest is higher than Maxent, and the AUC is 0.72.

Key words: landslide forecast, random forest, Maxent model, Jinsha River, susceptibility mapping

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