Yes / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19451 |
| Date | 17 May 2023 |
| Creators | Rye, Sara, Aktas, E. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | (c) 2023 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/), CC-BY |
Page generated in 0.0054 seconds