Articles | Volume 19, issue 11
Nat. Hazards Earth Syst. Sci., 19, 2541–2549, 2019
https://doi.org/10.5194/nhess-19-2541-2019
Nat. Hazards Earth Syst. Sci., 19, 2541–2549, 2019
https://doi.org/10.5194/nhess-19-2541-2019

Brief communication 19 Nov 2019

Brief communication | 19 Nov 2019

Machine learning analysis of lifeguard flag decisions and recorded rescues

Chris Houser et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (16 Sep 2019) by Mauricio Gonzalez
AR by Chris Houser on behalf of the Authors (18 Sep 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (24 Sep 2019) by Mauricio Gonzalez
RR by Alejandro Gutiérrez Echeverría (09 Oct 2019)
RR by Anonymous Referee #1 (15 Oct 2019)
ED: Publish as is (15 Oct 2019) by Mauricio Gonzalez
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Short summary
On many beaches, lifeguards set out flags to warn beach users of the surf and rip hazard based on the regional surf forecast and careful observation. There is a potential that the chosen flag does not accurately reflect the potential risk. Results of a machine learning analysis suggest that the greatest number of rescues occurred on days when the lifeguard flew a more cautious flag than the model predicted. It is argued that that beach users may be discounting lifeguard warnings.
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