Analysis and machine-learning-based prediction of beach accidents on a recreational beach in China

Abstract Beachgoers are sometimes exposed to coastal hazards, yet comprehensive analyses of characteristics and potential factors for beach accidents are rarely reported in China. In this study, information on beach accidents was collected on a recreational beach from 2004 to 2022 by searching the web or apps. The characteristics of beach accidents were therefore analysed in terms of age, gender, and activity of beachgoers. The potential factors were resolved in environmental aspects of meteorology, waves, tides, and beach morphology. Results show that beach accidents mainly occur in summer, with the highest occurrence in the afternoon and evening. The number of male beachgoers in accidents is five times higher than that of females. 90% of accidents occur when the beach is at a high-risk level for rip currents, providing evidence for the accuracy of the risk map built in a previous study. Three machine learning models, i.e., Support Vector Machine, Random Forest, and BP Neural Networks, are trained to predict beach accidents. The performances of these three machine learning algorithms are evaluated in terms of precision, recall, and F1 score. Support Vector Machine and BP Neural Networks significantly outperform Random Forest in terms of prediction. The accuracy in predicting "safe" and "dangerous" classes is approximately 80% of the Support Vector Machine model. This paper provides a preliminary study of machine learning based beach accident prediction for a specific tourist beach. In the future, machine learning will be applied to predict tourist beach accidents throughout mainland China.