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A fuzzy social vulnerability evaluation from the perception of disaster bearers against meteorological disasters

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  • Mei Cai

    (Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology)

  • Guo Wei

    (University of North Carolina At Pembroke)

Abstract

Because of climatic hazards and extreme weather events, meteorological disasters attract more and more attention of government, national, and international agencies. Every event tests people’s ability to cope with meteorological disasters and generates the need for disaster risk research and assessments. Social vulnerability is an important measure of disaster risk assessments. Social vulnerability assessment problem can be viewed as a multi-criteria decision-making problem. In order to satisfy the perception of special disaster bearers, we need a local-context approach to construct a social vulnerability evaluation index system. The key to this approach is to identify the evaluation criteria structure by analyzing the complicated information gathering from special disaster bearers. It’s natural to use fuzzy language to express disaster bearers’ preferences in a complicated context. This paper attempts to describe the interrelationship between the evaluation factors with linguistic preferences since linguistic variables can better reflect the vagueness of human being. The fuzzy interpretive structural modeling (FISM) approach has been employed to develop the structural relationship between social vulnerability evaluation factors. In FISM, we apply some computational models of computing with words to quantify the fuzzy interrelationship. Finally, we give an example to show the process of our method.

Suggested Citation

  • Mei Cai & Guo Wei, 2020. "A fuzzy social vulnerability evaluation from the perception of disaster bearers against meteorological disasters," 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. 103(2), pages 2355-2370, September.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:2:d:10.1007_s11069-020-04088-4
    DOI: 10.1007/s11069-020-04088-4
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    References listed on IDEAS

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    1. Ansari, Md. Fahim & Kharb, Ravinder Kumar & Luthra, Sunil & Shimmi, S.L. & Chatterji, S., 2013. "Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 163-174.
    2. Govindan, Kannan & Palaniappan, Murugesan & Zhu, Qinghua & Kannan, Devika, 2012. "Analysis of third party reverse logistics provider using interpretive structural modeling," International Journal of Production Economics, Elsevier, vol. 140(1), pages 204-211.
    3. Lisa Rygel & David O’sullivan & Brent Yarnal, 2006. "A Method for Constructing a Social Vulnerability Index: An Application to Hurricane Storm Surges in a Developed Country," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 11(3), pages 741-764, May.
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    Cited by:

    1. Mei Cai & Stephen M. Marson, 2021. "A regional Natech risk assessment based on a Natech-prone facility network for dependent events," 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. 107(3), pages 2155-2174, July.

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