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

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26/05/2023Keyword
Decision methodsDisaster response network
Disaster impact prediction
Disaster severity
Humanitarian aid network
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(c) 2023 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/)Peer-Reviewed
YesOpen Access status
openAccessAccepted for publication
17/05/2023
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This paper proposes a framework to cope with the lack of data at the time of a disaster by em-ploying predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely Moving Average (MA). A Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. A comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) up to 3% errors; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.Version
Published versionCitation
Rye S and Aktas E (2023) A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of PRED Model. Logistics. 7(2): 31.Link to Version of Record
https://doi.org/10.3390/logistics7020031Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.3390/logistics7020031