Institute for Disaster Management and Reconstruction, Sichuan University-Hongkong Polytechnic University, Chengdu, Sichuan, 610200, China.
Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
Sci Rep. 2019 Aug 29;9(1):12532. doi: 10.1038/s41598-019-48986-5.
A gradient boosting machine (GBM) was developed to model the susceptibility of debris flow in Sichuan, Southwest China for risk management. A total of 3839 events of debris flow during 1949-2017 were compiled from the Sichuan Geo-Environment Monitoring program, field surveys, and satellite imagery interpretation. In the cross-validation, the GBM showed better performance, with the prediction accuracy of 82.0% and area under curve of 0.88, than the benchmark models, including the Logistic Regression, the K-Nearest Neighbor, the Support Vector Machine, and the Artificial Neural Network. The elevation range, precipitation, and aridity index played the most important role in determining the susceptibility. In addition, the water erosion intensity, road construction, channel gradient, and human settlement sites also largely contributed to the formation of debris flow. The susceptibility map produced by the GBM shows that the spatial distributions of high-susceptibility watersheds were highly coupled with the locations of the topographical extreme belt, fault zone, seismic belt, and dry valleys. This study provides critical information for risk mitigating and prevention of debris flow.
为了进行风险管理，我们开发了一个梯度提升机（GBM）模型来对中国西南部四川省泥石流的易发性进行建模。从四川地质环境监测计划、实地调查和卫星图像解译中，共编制了 1949 年至 2017 年期间的 3839 次泥石流事件。在交叉验证中，GBM 的表现优于基准模型，包括逻辑回归、K 最近邻、支持向量机和人工神经网络，其预测准确率为 82.0%，曲线下面积为 0.88。高程范围、降水和干旱指数对确定易发性起着最重要的作用。此外，水蚀强度、道路建设、河道坡度和人类住区地点也极大地促成了泥石流的形成。由 GBM 生成的易发性图表明，高易发性流域的空间分布与地形极端带、断裂带、地震带和干谷的位置高度耦合。本研究为泥石流风险缓解和防治提供了关键信息。
