A rule-based predictive model for estimating human impact data in natural onset disasters—the case of a pred model

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dc.contributor.authorRye, Sara
dc.contributor.authorAktas, Emel
dc.date.accessioned2023-06-14T14:43:33Z
dc.date.available2023-06-14T14:43:33Z
dc.date.issued2023-05-26
dc.description.abstractBackground: 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.en_UK
dc.identifier.citationRye S, Aktas E. (2023) A rule-based predictive model for estimating human impact data in natural onset disasters—the case of a pred model. Logistics, Volume 7, Issue 2, May 2023, Article number 31en_UK
dc.identifier.issn2305-6290
dc.identifier.urihttps://doi.org/10.3390/logistics7020031
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19775
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdecision methodsen_UK
dc.subjectdisaster response networken_UK
dc.subjectdisaster impact predictionen_UK
dc.subjectdisaster severityen_UK
dc.subjecthumanitarian aid networken_UK
dc.titleA rule-based predictive model for estimating human impact data in natural onset disasters—the case of a pred modelen_UK
dc.typeArticleen_UK

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