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

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.