Designing intelligent disaster prediction models and systems for debris-flow disasters in Taiwan

Authors:
Hsu-Yang Kung;Chi-Hua Chen;Hao-Hsiang Ku
Affiliations:
Department of Management Information Systems, National Pingtung University of Science and Technology, 1 Shuefu Road, Neipu, Pingtung 912, Taiwan, ROC;Institute of Information Management, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, ROC;Department of Computer Science and Information Engineering, Hwa Hsia Institute of Technology, 111 Gong Jhuan Road, Chung Ho, Taipei 235, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Effective disaster prediction relies on using correct disaster decision model to predict the disaster occurrence accurately. This study proposes three effective debris-flow prediction models and an inference engine to predict and decide the debris-flow occurrence in Taiwan. The proposed prediction models are based on linear regression, multivariate analysis, and back-propagation networks. To create a practical simulation environment, the decision database is the pre-analyzed 181 potential debris-flows in Taiwan. According to the simulation results, the prediction model based on back-propagation networks predicted the debris flow most accurately. Moreover, a Real-timeMobileDebrisFlowDisasterForecastSystem (RM(DF)^2) was implemented as a three-tier architecture consisting of mobile appliances, intelligent situation-aware agents and decision support servers based on the wireless/mobile Internet communications. The RM(DF)^2 system provides real-time communication between the disaster area and the rescue-control center, and effectively prevents and manages debris-flow disasters.