Can the visualization of rip currents prevent drowning accidents? Consideration of the effect of optimism bias

Shintaro Endo (), Ryo Shimada (), Toshinori Ishikawa () and Tsutomu Komine ()
Additional contact information
Shintaro Endo: National Institution for Youth Education
Ryo Shimada: Chuo University
Toshinori Ishikawa: Chuo University
Tsutomu Komine: Chuo University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 110, issue 3, No 28, 2017-2033

Abstract: Abstract Drowning accidents at beach in Japan are caused by rip currents. To reduce these accidents, a new technology that can detect rip currents and notify beachgoers by using the Internet of Things (IoT) and Artificial Intelligence (AI) was proposed. However, studies on the effect of visualizing rip currents or considering the effect of optimism bias have not been conducted. This study investigates if visualization of rip currents might help in preventing drowning accidents, while considering the effect of optimism bias. The participants were 90 Japanese beachgoers. They were asked to answer questions based on their knowledge of the beach and rip currents, their optimism bias regarding rip currents, and awareness with or without visualization. The results of the analyses suggest that despite optimism bias, the visualization of rip currents increases the tendency of beachgoers to perceive and avoid rip currents. As described above, it was found that by visualizing the rip current, beachgoers were able to perceive and avoid rip currents. In addition, an understanding of rip currents is positively related to the intent to avoid rip currents even when rip currents are visualized. Therefore, it is necessary not only to enhance the avoidance tendency by visualizing rip currents, but also to further enhance knowledge of beachgoers to deepen the understanding of rip currents including the danger associated and methods to avoid them.

Keywords: Rip current; Beachgoers; Optimism bias; Hierarchical multiple regression analysis (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-05023-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:110:y:2022:i:3:d:10.1007_s11069-021-05023-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-05023-x

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().