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Natural Hazards

, Volume 91, Issue 1, pp 337–351 | Cite as

A model for the representation of emergency cases

Original Paper
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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.

Keywords

Emergency case Case representation Incident evolution Vulnerability of hazard-affected carriers Emergency response experience 

Notes

Acknowledgements

The authors deeply appreciate support for this paper by the National Natural Science Foundation of China (Grant No. 71673161), the Fundamental Research Funds for the Central Business Unit (Grant No. 512015Y-4002) and the Youth Talent Fund of Beijing (Grant No. 512016Z-4983).

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.China National Institute of StandardizationBeijingChina
  2. 2.School of Resources and Safety EngineeringChina University of Mining and TechnologyBeijingChina
  3. 3.School of Public AdministrationSichuan UniversityChengduChina
  4. 4.Institute for Public Safety ResearchTsinghua UniversityBeijingChina

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