Rye, SaraAktas, Emel2023-06-142023-06-142023-05-26Rye 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 312305-6290https://doi.org/10.3390/logistics7020031https://dspace.lib.cranfield.ac.uk/handle/1826/19775Background: 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/decision methodsdisaster response networkdisaster impact predictiondisaster severityhumanitarian aid networkArticle