Risk degree of debris flow applying neural networks |
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Authors: | Tung-Chiung Chang |
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Institution: | (1) Department of Civil Engineering, Kao-Yuan University, 1821 Chung-san Rd, Kaohsiung, Lu-chu Township, Kaohsiung County, 821, Taiwan, Republic of China |
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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. |
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Keywords: | Artificial neural networks Risk degree Debris flows Taiwan |
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