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

To predict beachgoer decision to enter the water at a high-energy surf beach, in southwest France.

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.

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.

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.