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A model for the representation of emergency cases

Author

Listed:
  • Chao Zhang

    (China National Institute of Standardization)

  • Jiansong Wu

    (China University of Mining and Technology)

  • Chao Huang

    (Sichuan University)

  • Bo Jiang

    (Tsinghua University)

Abstract

An emergency case contains important information and knowledge for emergency management, such as the evolution law of incidents, the vulnerability of hazard-affected carriers and the practice of emergency response. To respond effectively, we should learn from these valuable kinds of information and knowledge and utilize them. Most models of emergency cases are established based on ontology methodology. Important emergency information is semantically expressed and then analyzed by the text mining or cluster analysis methods. This type of methodology is at a disadvantage for obtaining the knowledge contained in the cases. In addition, some emergency case representation models are established using the event tree method or state chart method. However, not all the important information for emergencies can be integrated into these diagrams. The knowledge elements are not expressed with good structure, which results in a disadvantage to case-based reasoning and knowledge mining. In this paper, a comprehensive model for the representation of emergency cases is established. The proposed model combines the advantages of several conventional methods, including event tree, Bayesian conditional probability and information structured expression. Hazard-affected carrier properties, incident evolution laws and emergency response experience can be integrated and represented, which provides a good basis to employ data mining technology. With the proposed model, the general laws and successful emergency response experience contained in massive emergency cases can be obtained. Furthermore, the case-based reasoning and knowledge mining models for risk assessment, emergency preparedness and prevention, and decision-making can be developed based on effectively represented emergency cases.

Suggested Citation

  • Chao Zhang & Jiansong Wu & Chao Huang & Bo Jiang, 2018. "A model for the representation of emergency cases," 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. 91(1), pages 337-351, March.
    • Handle: RePEc:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-3131-9
    DOI: 10.1007/s11069-017-3131-9
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    References listed on IDEAS

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    1. Buzna, Lubos & Peters, Karsten & Helbing, Dirk, 2006. "Modelling the dynamics of disaster spreading in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 132-140.
    2. Ahmet Ozan Celik & Volkan Kiricci & Canberk Insel, 2017. "Reassessment of the flood damage at a river diversion hydropower plant site: lessons learned from a case study," 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. 86(2), pages 833-847, March.
    3. K. Peters & L. Buzna & D. Helbing, 2008. "Modelling of cascading effects and efficient response to disaster spreading in complex networks," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 4(1/2), pages 46-62.
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    Cited by:

    1. Zheng Tang & Yijia Li & Xiaofeng Hu & Huanggang Wu, 2019. "Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    2. Xiao Zhang & Xiaofeng Hu & Yiping Bai & Jiansong Wu, 2020. "Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network," IJERPH, MDPI, vol. 17(2), pages 1-18, January.

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