Neural network modeling for regional hazard assessment of debris flow in Lake Qionghai Watershed, China

Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine

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.