Risk degree of debris flow applying neural networks

Tung-Chiung Chang ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2007, vol. 42, issue 1, 209-224

Abstract: A number of methods for prediction of debris flows have been studied. However, the successful prediction ratios of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for risk degree predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 171 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 99.12% demonstrates that the presented ANN model with seven significant factors can provide a highly stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems. Copyright Springer Science+Business Media, Inc. 2007

Keywords: Artificial neural networks; Risk degree; Debris flows; Taiwan (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11069-006-9069-y

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