Articles | Volume 19, issue 11
Nat. Hazards Earth Syst. Sci., 19, 2541–2549, 2019
Nat. Hazards Earth Syst. Sci., 19, 2541–2549, 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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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. 
Barrett, G. and Houser, C.: Identifying hotspots of rip current activity using wavelet analysis at Pensacola Beach, Florida, Phys. Geogr., 33, 32–49, 2012. 
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. 
Brander, R. W., Bradstreet, A., Sherker, S., and MacMahan, J.: Responses of swimmers caught in rip currents: Perspectives on mitigating the global rip current hazard, Int.J. Aquat. Res. Educ., 5, 11, 2011. 
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.
Final-revised paper