Comprehensive assessment of debris flow hazard risk is a challenging task due to the uncertainties and complexity of various related factors. A reasonable and reliable assessment should be based on sufficient data and assessment approaches reflecting actual situation.

Researchers from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR-CAS) proposed a novel approach for assessing debris flow hazard risk based on BN (Bayesian Network) and domain knowledge is presented

Based on authoritative records of debris flow hazardsand geomorphologic and environmental data forthe Chinese mainland, approaches based on BN, SVM (Support Vector Machine) and ANNs (Artificial Neural Networks) were compared.

The results show that BN provides a higher probability (85.66%) of hazard detection, a better precision (89.63%), and a bigger AUC (area under the receiver operating characteristic curve) value (0.95) than SVM and ANNs. The BN-based model is useful for mapping and assessing debris flow hazard risk on a national scale.

The project is supported by grants from the Ministry of Science and Technology of China (No. 2008BAK50B01) and State Key Laboratory of Resources and Environmental Information Systems. The result of the research has been published on the Geomorphology (2012, DOI:j.geomorph.2012.05.008).

CONTACT:
JIANG Dong
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Beijing, China
E-mail: jiangd@igsnrr.ac.cn