Show simple item record

dc.contributor.authorRye, Sara
dc.contributor.authorAktas, E.
dc.date.accessioned2023-05-17T10:38:55Z
dc.date.accessioned2023-06-12T09:29:53Z
dc.date.available2023-05-17T10:38:55Z
dc.date.available2023-06-12T09:29:53Z
dc.date.issued2023-05-26
dc.identifier.citationRye 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.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19451
dc.descriptionYesen_US
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisherMDPI
dc.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/)en_US
dc.subjectDecision methodsen_US
dc.subjectDisaster response networken_US
dc.subjectDisaster impact predictionen_US
dc.subjectDisaster severityen_US
dc.subjectHumanitarian aid networken_US
dc.titleA Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters - The Case of PRED Modelen_US
dc.status.refereedYesen_US
dc.date.Accepted2023-05-17
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.identifier.doihttps://doi.org/10.3390/logistics7020031
dc.rights.licenseCC-BYen_US
dc.date.updated2023-05-17T10:38:57Z
refterms.dateFOA2023-06-12T09:30:22Z
dc.openaccess.statusopenAccessen_US


Item file(s)

Thumbnail
Name:
rye_and_aktas_2023.pdf
Size:
627.4Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record