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

Sara Rye () and Emel Aktas
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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

Logistics, 2023, vol. 7, issue 2, 1-24

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.

Keywords: decision methods; disaster response network; disaster impact prediction; disaster severity; humanitarian aid network (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2023
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