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

Logistics

article

Sara Rye¹  Emel Aktas² 

[1]School of Social Sciences, Faculty of Management, Law and Social Sciences, University of Bradford

[2]Cranfield School of Management, Cranfield University, College Road

关键词: decision methods;    disaster response network;    disaster impact prediction;    disaster severity;    humanitarian aid network;   

DOI  :  10.3390/logistics7020031

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Background: 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.

【 授权许可】

CC BY