Predicting individual’s decision to enter the water at a high-energy recreational surf beach in France

Jeoffrey Dehez (), Sandrine Lyser () and Bruno Castelle ()
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Jeoffrey Dehez: UR ETTIS - Environnement, territoires en transition, infrastructures, sociétés - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Sandrine Lyser: UR ETTIS - Environnement, territoires en transition, infrastructures, sociétés - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Bruno Castelle: EPOC - Environnements et Paléoenvironnements OCéaniques - EPHE - École Pratique des Hautes Études - PSL - Université Paris Sciences et Lettres - UB - Université de Bordeaux - INSU - CNRS - Institut national des sciences de l'Univers - CNRS - Centre National de la Recherche Scientifique

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Abstract: Objectives To predict beachgoer decision to enter the water at a high-energy surf beach, in southwest France. Methods We built a unique multidisciplinary database combining data collected by an on-site beachgoers survey, weather stations, marine buoys and tidal reconstruction. Human, weather and meteocean factors were considered as potentially predictive of beachgoer behaviour. We employed a logistic regression analysis to predict beachgoers' decision to enter the water on any given day at a high-energy recreational beach. Results We demonstrated that both environmental and human factors influence a beachgoer's decision to enter the water. Daily mean wave height and daily mean insolation duration were significant predictors at the p<0.001 level, while age, place of residence and self-confidence in swimming out of a rip current were significant at the p<0.05 level or higher. Beachgoers were more likely to enter the water on sunny days with lower waves. Younger individuals, those living outside the Landes département, and those who declared themselves to be ‘confident' or ‘uncertain' about their ability to swim out of a rip current expressed a higher propensity to enter the water. Our model has an accuracy, F-Score, precision and recall of 71%, 73%, 86%, 79%, respectively. Conclusions Beachgoer exposure on any given day can ultimately be predicted by coupling our model with beach attendance models. This would allow for the design of rescue and preventive operations on days with high expected exposure. While models based solely on environmental factors can be used to forecast beach risks, incorporating human factors into the model provides valuable insight for crafting prevention messages. In this regard, lifeguards could engage more actively with beach users to deliver appropriate safety messages.

Keywords: Drowning prevention; Risk forecasting; Beach Safety (search for similar items in EconPapers)
Date: 2025-05-08
Note: View the original document on HAL open archive server: https://hal.science/hal-05064341v1
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Published in Injury Prevention, 2025, ip-2024-045574. ⟨10.1136/ip-2024-045574⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05064341

DOI: 10.1136/ip-2024-045574

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