Information Entropy Embedded Back Propagation Neural Network Approach for Debris Flows Hazard Assessment

Abstract

The purpose of the study is to propose a new method to assess debris flow hazard by embedding Shannon’s information entropy in back propagation neural network. The procedure was divided into two parts. One was to establish a mathematical model for debris-flow hazard assessment based on Shannon’s information entropy forming input and output data sets of back propagation neural network. The other was to establish a back propagation neural network technique for characterizing debris flow dynamic hazard. The proposed method was employed to assess debris flow hazard of Shenxi gully basin, Sichuan province, China. The result shows that the assessed result of proposed method is highly consistent with the result of field surveys. The proposed method can reflect the interaction, nonlinearity and dynamic process of background variable factors and can be used to debris flow hazard assessment, risk management and mitigation of debris flows.