What matters the most? Understanding individual tornado preparedness using machine learning

Junghwa Choi (), Scott Robinson (), Romit Maulik () and Wesley Wehde ()
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Junghwa Choi: The University of Oklahoma
Scott Robinson: The University of Oklahoma
Romit Maulik: Argonne National Laboratory
Wesley Wehde: East Tennessee State University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 103, issue 1, No 54, 1183-1200

Abstract: Abstract Scholars from various disciplines have long attempted to identify the variables most closely associated with individual preparedness. Therefore, we now have much more knowledge regarding these factors and their association with individual preparedness behaviors. However, it has not been sufficiently discussed how decisive many of these factors are in encouraging preparedness. In this article, we seek to examine what factors, among the many examined in previous studies, are most central to engendering emergency preparedness in individuals particularly for tornadoes by utilizing a relatively uncommon machine learning technique in disaster management literature. Using unique survey data, we find that in the case of tornado preparedness the most decisive variables are related to personal experiences and economic circumstances rather than basic demographics. Our findings contribute to scholarly endeavors to understand and promote individual tornado preparedness behaviors by highlighting the variables most likely to shape tornado preparedness at an individual level.

Keywords: Disaster management; Emergency preparedness; Machine learning; Random forest regression; Tornado preparedness (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11069-020-04029-1

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