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

Junghwa Choi, Scott Robinson, Romit Maulik, Wesley Wehde

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1183-1200
Number of pages18
JournalNatural Hazards
Issue number1
StatePublished - Aug 1 2020

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)

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