Analysis and machine-learning-based prediction of beach accidents on a recreational beach in China DOI Open Access
Yuan Li, Jiqiang Tang, Chi Zhang

и другие.

Anthropocene Coasts, Год журнала: 2024, Номер 7(1)

Опубликована: Дек. 30, 2024

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

Язык: Английский

Analysis and machine-learning-based prediction of beach accidents on a recreational beach in China DOI Open Access
Yuan Li, Jiqiang Tang, Chi Zhang

и другие.

Anthropocene Coasts, Год журнала: 2024, Номер 7(1)

Опубликована: Дек. 30, 2024

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

Язык: Английский

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