Articles | Volume 19, issue 11
https://doi.org/10.5194/nhess-19-2541-2019
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, Jacob Lehner, Nathan Cherry, and Phil Wernette

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Cited articles

Althuwaynee, O. F., Pradhan, B., Park, H. J., and Lee, J. H.: A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping, Landslides, 11, 1063–1078, 2014. 
Arozarena, I., Houser, C., Echeverria, A. G., and Brannstrom, C.: The rip current hazard in Costa Rica, Nat. Hazards, 77, 753–768, 2015. 
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Bielders, C. L., Ramelot, C., and Persoons, E.: Farmer perception of runoff and erosion and extent of flooding in the silt-loam belt of the Belgian Walloon Region, Environ. Sci. Policy, 6, 85–93, 2003. 
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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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