Title: A novel taxonomy of natural disasters based on casualty and consequence using hierarchical clustering

Authors: Donald Douglas Atsa'am; Frank Adusei-Mensah; Oluwafemi Samson Balogun; Temidayo Oluwatosin Omotehinwa; Oluwaseun Alexander Dada; Richard Osei Agjei; Samuel Nii Odoi Devine

Addresses: Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, QwaQwa Campus, South Africa ' Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Centre for Multidisciplinary Research and Innovation, Abuja, Nigeria ' School of Computing, University of Eastern Finland, Kuopio Campus FI-70211, Finland; Centre for Multidisciplinary Research and Innovation, Abuja, Nigeria ' Department of Mathematics and Computer Science, Federal University of Health Sciences, Otukpo, Nigeria ' Department of Computer Science, University of Helsinki, Helsinki, Finland; The School of Software, Lekki-Lagos, Nigeria ' Department of Health Administration and Education, University of Education, Winneba, Ghana ' Department of Information and Communication Technology, Presbyterian University Ghana, Abetifi, Ghana

Abstract: Post-disaster management requires a proportional deployment of human and material resources. The number of resources required to manage a disaster cannot be known without first evaluating the extent of casualty and consequence. This study proposed a taxonomy for classifying natural disasters based on casualty and consequence. Using a secondary data on global disasters from 1900 to 2021, the hierarchical cluster analysis technique was deployed for taxonomy formation. The learning algorithm evaluated the similarities in numbers of deaths, injuries, and the cost of damaged property caused by disasters. Three clusters were extracted which sub-grouped historical disasters based on similarities in casualty and consequence. Further, a taxonomy that defines the ranges of what constitute low, average, and high deaths/injuries/damage was established. Classifying a future disaster with this taxonomy prior to the deployment of resources for rescue, resettlement, compensation, and other disaster management operations will guide efficient resource allocation on a case-by-case basis.

Keywords: disaster taxonomy; natural disasters; casualty and consequence; post-disaster management; hierarchical cluster analysis.

DOI: 10.1504/IJDMMM.2023.134591

International Journal of Data Mining, Modelling and Management, 2023 Vol.15 No.4, pp.313 - 330

Received: 08 Sep 2022
Accepted: 05 Mar 2023
Published online: 30 Oct 2023 *

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