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

被引:0
|
作者
Junghwa Choi
Scott Robinson
Romit Maulik
Wesley Wehde
机构
[1] The University of Oklahoma,Henry Bellmon Chair of Public Service
[2] The University of Oklahoma,Argonne Leadership Computing Facility
[3] Argonne National Laboratory,undefined
[4] East Tennessee State University,undefined
来源
Natural Hazards | 2020年 / 103卷
关键词
Disaster management; Emergency preparedness; Machine learning; Random forest regression; Tornado preparedness;
D O I
暂无
中图分类号
学科分类号
摘要
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.
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页码:1183 / 1200
页数:17
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