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Risk degree of debris flow applying neural networks

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  • Tung-Chiung Chang

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

Suggested Citation

  • Tung-Chiung Chang, 2007. "Risk degree of debris flow applying neural networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 42(1), pages 209-224, July.
    • Handle: RePEc:spr:nathaz:v:42:y:2007:i:1:p:209-224
    DOI: 10.1007/s11069-006-9069-y
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    Citations

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    Cited by:

    1. Shahram Kaboodvandpour & Jamil Amanollahi & Samira Qhavami & Bakhtiyar Mohammadi, 2015. "Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(2), pages 879-893, September.
    2. Yan-Ning Wang & Han Chen & Bin-Song Jiang & Jing-Rui Peng & Jun Chen, 2022. "Cause Analysis and Preventive Measures of Guizhou D2809 Train Derailment Accident in Guizhou, China on 4 June 2022," IJERPH, MDPI, vol. 19(24), pages 1-14, December.
    3. Bin Yu & Yuan Zhu & Tao Wang & Yuanjing Chen & Yunbo Zhu & Yongbo Tie & Ke Lu, 2014. "A prediction model for debris flows triggered by a runoff-induced mechanism," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 1141-1161, November.
    4. Yong-Jun Lin & Yuan-Hsiou Chang & Yih-Chi Tan & Hong-Yuan Lee & Yu-Jia Chiu, 2011. "National policy of watershed management and flood mitigation after the 921 Chi-Chi earthquake in Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 56(3), pages 709-731, March.
    5. Cencen Niu & Qing Wang & Jianping Chen & Wen Zhang & Liming Xu & Ke Wang, 2015. "Hazard Assessment of Debris Flows in the Reservoir Region of Wudongde Hydropower Station in China," Sustainability, MDPI, vol. 7(11), pages 1-20, November.
    6. Chen Cao & Peihua Xu & Jianping Chen & Lianjing Zheng & Cencen Niu, 2016. "Hazard Assessment of Debris-Flow along the Baicha River in Heshigten Banner, Inner Mongolia, China," IJERPH, MDPI, vol. 14(1), pages 1-19, December.
    7. Bin Yu & Li Li & Yufu Wu & Shengming Chu, 2013. "A formation model for debris flows in the Chenyulan River Watershed, Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 745-762, September.
    8. Wen Zhang & Jian-ping Chen & Qing Wang & Yuke An & Xin Qian & Liangjun Xiang & Longxiang He, 2013. "Susceptibility analysis of large-scale debris flows based on combination weighting and extension methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 66(2), pages 1073-1100, March.

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