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01-06-2015 | Issue 2/2015

Cluster Computing 2/2015

Ontology-driven slope modeling for disaster management service

Journal:
Cluster Computing > Issue 2/2015
Authors:
Hoill Jung, Kyungyong Chung

Abstract

These days, with the development of information technology, new paradigms have been created through academical and technological convergence in various areas. The IT convergence draws much attention as the next generation technology for disaster prevention and management in the construction and transportation area. Along with global warming, global climate changes and unusual weather occur around the world, and consequently disasters become more huge. IT convergence based disaster management service makes it possible to quickly respond to unexpected disasters in the ubiquitous environment and mitigate the disasters. Although research on disaster prevention and management has constantly been conducted, it is relatively slow to develop the technology for disaster prediction and prevention. For efficient safety and disaster prevention and management in the next generation IT convergence, it is essential to establish a systematic disaster prevention technology and a disaster prevention information system. In this paper, we proposed ontology-driven slope modeling for disaster management service through the convergence of construction, transportation technology and IT. User profile, environment information, location information, weather index, slope stability, disaster, statistics and analysis of disasters, and forest fire disaster index are used to build internal context information, external context information, and service context information. Ontology-based context awareness modeling of the landslides and disasters generated is constructed, and relevant rules are generated by inference engine. Based on the ontology of external and internal context awareness, the rules of service inference derived by inference engine are produced using protégé 5.0. According to the service inference rules, disaster control services best fitting for users’ environment is provided. By addressing the social issues related to disaster prevention and response and judging the potential risk of disasters, the proposed method can contribute to improving the safety of the public and the quality of their life. Social consensus on the necessity of prevention of urban climate disasters can be formed easily, and a ripple effect is expected on the situational response to natural disaster.

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