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

; 
Aktas, E.
Publication Date
26/05/2023
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Keywords
Decision methods, Disaster response network, Disaster impact prediction, Disaster severity, Humanitarian aid network
Rights
(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/)
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Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
17/05/2023
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Life Sciences Publications
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rye_and_aktas_2023.pdf
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Abstract
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.
URI
http://hdl.handle.net/10454/19451
Version
Published version
Citation
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.
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Link to Version of Record
https://doi.org/10.3390/logistics7020031
Type
Article
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