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A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model

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  • Sara Rye

    (School of Social Sciences, Faculty of Management, Law and Social Sciences, University of Bradford, Richmond Rd., Bradford BD7 1DP, UK)

  • Emel Aktas

    (Cranfield School of Management, Cranfield University, College Road, Cranfield MK43 0AL, UK)

Abstract

Background: This paper proposes a framework to cope with the lack of data at the time of a disaster by employing predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. Methods : A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely the Moving Average (MA). Results: Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. Conclusions: comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) with up to 3% error; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:2:p:31-:d:1156781
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    References listed on IDEAS

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