Preliminary and Fast Risk Assessment of Debris-Flow Hazards Using Different Artificial Neural Networks
Journal Title: International Journal of Environment, Ecology, Family and Urban Studies (IJEEFUS) - Year 2017, Vol 7, Issue 1
Abstract
Researches revealed that prediction of occurrence of debris-flow hazards can be achieved by artificial intelligence techniques. In the paper three different artificial neural networks (ANNs), i.e., back-propagation neural network (BPNN), radial basis function neural network (RBFNN) and probabilistic neural network (PNN), are employed for risk assessment of debris-flow hazards. Case of watershed area of Chen-Yu-Lan River in Nantou, Taiwan, studied by Hsiao (2003) who employed an integrated technique of digital terrain model (DTM), image processing schemes, and geographical information system (GIS) is considered. ANN models with 15 influence factors and with 7 influence factors obtained by principal component analysis (PCA) are tested, respectively. In the BPNN topology four different learning rules of MATLAB nntool are selected. It is shown that the percentage of accuracy of debris-flow hazard prediction can be up to 90.48% using BPNN models with totally 15 influence factors. Present study revealed that integration of DTM, GIS and ANN techniques can construct a preliminary and fast risk asse assessment of debris flow hazards for watershed areas.
Authors and Affiliations
Li-Jeng Huang, Tzy-Wei Chen
Keywords
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