Using ADABOOST and Rough Set Theory for Predicting Debris Flow Disaster

Ping-Feng Pai (), Lan-Lin Li, Wei-Zhan Hung and Kuo-Ping Lin

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 4, 1143-1155

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

Keywords: Debris flow; ADABOOST; Rough set theory; Prediction; Rule generation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-014-0548-8

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