Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

Abstract

An accurate assessment of debris-flow susceptibility is of great importance to the prevention and control of debris-flow disasters in mountainous areas. In this study, by applying the recursive feature elimination-random forest (RFE-RF) and the stacking ensemble learning including multiple heterogeneous learners, debris-flow the high accuracy of the debris-flow susceptibility is assessed. As indicated by the results, the very low and low susceptibility zones of debris-flow are mainly concentrated in the eastern and western parts in the study area. The very high and high susceptibility zones are mainly distributed on the two banks of Xiaojiang River Valley and the south bank of Jinsha River, where there is fragile geological environment and high risk, in the study area. The medium susceptibility zone is mainly distributed around the very high and high susceptibility zones. There are excellent accuracy and stability in the stacking ensemble learning model of debris-flow susceptibility in the mountainous areas, when combining with the RFE-RF model and the diversity measurement. As for the stacking ensemble learning therein, the area under curve (AUC) value of the receiver-operating characteristic (ROC), the accuracy (ACC) value, and F1 score are the maximum, reaching 95.6%, 88.6%, and 88.9%, respectively. Besides, the root mean square error (RMSE) value is the minimum, namely 0.287, which indicates that stacking ensemble learning including multiple heterogeneous learners is a high-performance model for debris-flow susceptibility assessment. The findings can provide a scientific basis for the disaster prevention and mitigation in the mountainous areas.