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Using ADABOOST and Rough Set Theory for Predicting Debris Flow Disaster

Author

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  • Ping-Feng Pai
  • Lan-Lin Li
  • Wei-Zhan Hung
  • Kuo-Ping Lin

Abstract

Debris flow resulting from typhoons, heavy rainfall, tsunamis or other natural disasters is a matter of particular importance to Taiwan owing to the country’s unique geographical environment and exacerbated by poor slope management and global warming. With regard to these types of natural occurrences, recent global events have attracted the attention of experts in various fields, such as civil engineering, environmental engineering and information management. These experts have developed several techniques to study the various factors of debris flow. The ADABOOST and rough set theory (RST) are two emerging methods with regard to classification and rule provision. The ADABOOST, an adaptive boosting machine learning algorithm, uses very little memory during computation and can obtain robust classification results. RST is able to deal with uncertainties and vague information in generating rules for decision makers. Thus, this study develops an ADARST model which uses the unique strengths of the ADABOOST and RST in classification and rule generation and applies the proposed ADARST to analyze debris flow. Specifically, data from previous studies were obtained and used for the purposes of this study. Experimental results have shown that the proposed ADARST model is able to generate better results than those in previous investigations in terms of prediction accuracy. In addition, the designed ADARST model can provide rules including forward and backward reasoning ways for decision makers. Therefore, the proposed ADARST model is shown to be an effective methodology with which to analyze debris flow. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Ping-Feng Pai & Lan-Lin Li & Wei-Zhan Hung & Kuo-Ping Lin, 2014. "Using ADABOOST and Rough Set Theory for Predicting Debris Flow Disaster," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1143-1155, March.
  • Handle: RePEc:spr:waterr:v:28:y:2014:i:4:p:1143-1155
    DOI: 10.1007/s11269-014-0548-8
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    References listed on IDEAS

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    1. Yuyu Liu & Caizhi Sun & Shiguo Xu, 2013. "Eco-Efficiency Assessment of Water Systems in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4927-4939, November.
    2. Yong-Ying Zhu & Hui-Cheng Zhou, 2009. "Rough Fuzzy Inference Model and its Application in Multi-factor Medium and Long-term Hydrological Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 493-507, February.
    3. Ping-Feng Pai & Fong-Chuan Lee, 2010. "A Rough Set Based Model in Water Quality Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2405-2418, September.
    4. Sanchis, A. & Segovia, M.J. & Gil, J.A. & Heras, A. & Vilar, J.L., 2007. "Rough Sets and the role of the monetary policy in financial stability (macroeconomic problem) and the prediction of insolvency in insurance sector (microeconomic problem)," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1554-1573, September.
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    Cited by:

    1. Maryam Zavareh & Viviana Maggioni, 2018. "Application of Rough Set Theory to Water Quality Analysis: A Case Study," Data, MDPI, vol. 3(4), pages 1-15, November.
    2. Huaizhi Su & Meng Yang & Yeyuan Kang, 2016. "Comprehensive Evaluation Model of Debris Flow Risk in Hydropower Projects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1151-1163, February.
    3. Ling Tan & Ji Guo & Selvarajah Mohanarajah & Kun Zhou, 2021. "Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices," 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. 107(3), pages 2389-2417, July.

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