金沙江流域滑坡易发性空间预报分析

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。

Analysis and prediction of landslide susceptibility in Jinsha River

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.