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

IF 3.6 Q2 MANAGEMENT
Sara Rye, E. Aktas
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Abstract

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.
自然灾害中基于规则的人类影响预测模型——以PRED模型为例
背景:本文提出了一个框架,以应对灾害时的数据缺乏,采用预测模型。该框架可用于根据受灾国的社会经济特点评估灾害对人的影响。方法:采用概念漂移现象和基于规则的移动平均(MA)分类器对1980 - 2020年间4252起自然灾害的面板数据进行处理。结果:基于灾害严重性分析(DSA)技术,建立了预测数据预测模型(PRED)作为决策平台。结论:与真实数据对比表明,该平台可以预测灾难对人类的影响(死亡、受伤、无家可归),误差不超过3%;因此,它能够为各种灾害场景的救灾合作伙伴的选择提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Logistics-Basel
Logistics-Basel Multiple-
CiteScore
6.60
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审稿时长
11 weeks
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