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

被引:6
|
作者
Choi, Junghwa [1 ]
Robinson, Scott [2 ]
Maulik, Romit [3 ]
Wehde, Wesley [4 ]
机构
[1] Univ Oklahoma, 455 West Lindsey,205 DAHT, Norman, OK 73019 USA
[2] Univ Oklahoma, Henry Bellmon Chair Publ Serv, 455 West Lindsey,304E DAHT, Norman, OK 73019 USA
[3] Argonne Natl Lab, Argonne Leadership Comp Facil, Lemont, IL 60439 USA
[4] East Tennessee State Univ, 301C Rogers Stout Hall,POB 70651, Johnson City, TN 37614 USA
关键词
Disaster management; Emergency preparedness; Machine learning; Random forest regression; Tornado preparedness; EMERGENCY PREPAREDNESS; UNITED-STATES; DISASTER PREPAREDNESS; HAZARD; RISK; GOVERNMENT; PERCEPTIONS; FAMILIES; BEHAVIOR; CHILDREN;
D O I
10.1007/s11069-020-04029-1
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
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.
引用
收藏
页码:1183 / 1200
页数:18
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