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Relief demand forecasting based on intuitionistic fuzzy case-based reasoning

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  • Shao, Jianfang
  • Liang, Changyong
  • Liu, Yujia
  • Xu, Jian
  • Zhao, Shuping

Abstract

Prediction for demand of relief materials is a fundamental condition of disaster relief operations, and the premise for optimal allocation of emergency resources. There are currently few research papers about demand forecasting at home and abroad. Looking at the characteristics of relief supply demand prediction with incomplete and inaccurate available information, and uncertainty of environment, we propose a demand prediction method called intuitionistic fuzzy case-based reasoning (IFCBR). This method combines the advantages of intuitionistic fuzzy theory and case-based reasoning (CBR). Also proposed in this paper are similarity calculation methods and a new weight calculation method. A case study is addressed to illustrate the prediction process of relief demand using the proposed method. Finally, the validity of the method is verified by an empirical evaluation experiment in which actual earthquake disaster cases are introduced. This forecasting method provides decision support for relief material requirements, and provides a basis for resource allocation.

Suggested Citation

  • Shao, Jianfang & Liang, Changyong & Liu, Yujia & Xu, Jian & Zhao, Shuping, 2021. "Relief demand forecasting based on intuitionistic fuzzy case-based reasoning," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:soceps:v:74:y:2021:i:c:s0038012119302472
    DOI: 10.1016/j.seps.2020.100932
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    Cited by:

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    3. Sara Rye & Emel Aktas, 2023. "A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model," Logistics, MDPI, vol. 7(2), pages 1-24, May.

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