Intelligent and situation-aware pervasive system to support debris-flow disaster prediction and alerting in Taiwan

Authors:
Hsu-Yang Kung;Hao-Hsiang Ku;Che-I Wu;Ching-Yu Lin
Affiliations:
National Pingtung University of Science and Technology, Department of Management Information Systems, 1 Hsueh Fu Road, Nei Pu Hsiang, Pingtung 91207, Taiwan;National Pingtung University of Science and Technology, Department of Management Information Systems, 1 Hsueh Fu Road, Nei Pu Hsiang, Pingtung 91207, Taiwan;Graduate Institute of Disaster Prevention on Hillslopes and Water Resources Engineering, National Pingtung University of Science and Technology, 1 Hsueh Fu Road, Nei Pu Hsiang, Pingtung 91207, Tai ...;National Pingtung University of Science and Technology, Department of Management Information Systems, 1 Hsueh Fu Road, Nei Pu Hsiang, Pingtung 91207, Taiwan
Venue:
Journal of Network and Computer Applications
Year:
2008

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Abstract

Effective information transmission through robust communications is critical to prevent and alert for natural disasters. However, disasters always destroy the wired communication environment. Moreover, effective information needs to reveal the real situations of the disaster, e.g., the accurate position and the real-time image/video of accident events. An accurate disaster prediction model is useful to reduce casualties and prevent disasters from occurring. An effective disaster prediction is based on the accurate disaster decision model, which can be achieved through the situation-aware information communications between the disaster area and the rescue-control center. This study proposes and designs an Intelligent and Situation-Aware Pervasive System (ISPS), which successfully alert people the occurrence of debris-flow disasters. ISPS is a three-tier architecture consisting of mobile appliances, intelligent situation-aware agents (ISA) and a decision support server based on the wireless/mobile Internet communications. Furthermore, the Location-aware Routing Prediction Method (LRPM) was developed to decrease the transmission traffic and latency of pictures pushing the maps of the disaster to mobile clients. Based on the database of the pre-analyzed 181 potential debris flows in Taiwan, accurate debris flow prediction models were built to prevent debris flow using case-based reasoning (CBR) in the decision support server.