Assessment of debris flow hazards using a Bayesian Network

Abstract

Comprehensive assessment of debris flow hazard risk is challenging due to the complexity and uncertainties of various related factors. A reasonable and reliable assessment should be based on sufficient data and realistic approaches. This study presents a novel approach for assessing debris flow hazard risk using BN (Bayesian Network) and domain knowledge. Based on the records of debris flow hazards and geomorphological/environmental data for the Chinese mainland, approaches based on BN, SVM (Support Vector Machine) and ANN (Artificial Neural Network) were compared. BN provided the highest values of hazard detection probability, precision, and AUC (area under the receiver operating characteristic curve). The BN model is useful for mapping and assessing debris flow hazard risk on a national scale.