Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting
Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating...
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1181 |
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| author | Bruno Castelle David Carayon Jeoffrey Dehez Sylvain Liquet Vincent Marieu Nadia Sénéchal Sandrine Lyser Jean-Philippe Savy Stéphanie Barneix |
| author_facet | Bruno Castelle David Carayon Jeoffrey Dehez Sylvain Liquet Vincent Marieu Nadia Sénéchal Sandrine Lyser Jean-Philippe Savy Stéphanie Barneix |
| author_sort | Bruno Castelle |
| collection | DOAJ |
| description | Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, waves, tide, and time (e.g., day hour, weekday). This approach is applied to data collected from Biscarrosse Beach during the summer of 2023, where beach attendance varied significantly (from 0 to 2031 individuals). Results indicate that the optimal XGBoost model achieved high predictive accuracy, with a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.97 and an RMSE of 70.4 users, using daily mean weather data, tide and time as input variables, i.e., disregarding wave data. The model skilfully captures both day-to-day and hourly variability in attendance, with time of day (hour) and daily mean air temperature being the most influential variables. An XGBoost model using only daily mean temperature and hour of the day even shows good predictive accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.90). The study emphasises the importance of daily mean weather data over instantaneous measurements, as beach users tend to plan visits based on forecasts. This model offers reliable, computationally inexpensive, and high-frequency (e.g., every 10 min) beach user predictions which, combined with existing surf-zone hazard forecast models, can be used to anticipate life risk at the beach. |
| format | Article |
| id | doaj-art-1feb1856730e496dab68ea500854db2e |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-1feb1856730e496dab68ea500854db2e2025-08-20T02:21:13ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01136118110.3390/jmse13061181Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived CountingBruno Castelle0David Carayon1Jeoffrey Dehez2Sylvain Liquet3Vincent Marieu4Nadia Sénéchal5Sandrine Lyser6Jean-Philippe Savy7Stéphanie Barneix8Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, 33615 Pessac, FranceINRAE Nouvelle Aquitaine, Cestas-Gazinet, 33140 Villenave-d’Ornon, FranceINRAE Nouvelle Aquitaine, Cestas-Gazinet, 33140 Villenave-d’Ornon, FranceMétéo-France, 31055 Toulouse, FranceUniv. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, 33615 Pessac, FranceUniv. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, 33615 Pessac, FranceINRAE Nouvelle Aquitaine, Cestas-Gazinet, 33140 Villenave-d’Ornon, FranceSMGBL, 40660 Messanges, FranceSMGBL, 40660 Messanges, FranceAccurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, waves, tide, and time (e.g., day hour, weekday). This approach is applied to data collected from Biscarrosse Beach during the summer of 2023, where beach attendance varied significantly (from 0 to 2031 individuals). Results indicate that the optimal XGBoost model achieved high predictive accuracy, with a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.97 and an RMSE of 70.4 users, using daily mean weather data, tide and time as input variables, i.e., disregarding wave data. The model skilfully captures both day-to-day and hourly variability in attendance, with time of day (hour) and daily mean air temperature being the most influential variables. An XGBoost model using only daily mean temperature and hour of the day even shows good predictive accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.90). The study emphasises the importance of daily mean weather data over instantaneous measurements, as beach users tend to plan visits based on forecasts. This model offers reliable, computationally inexpensive, and high-frequency (e.g., every 10 min) beach user predictions which, combined with existing surf-zone hazard forecast models, can be used to anticipate life risk at the beach.https://www.mdpi.com/2077-1312/13/6/1181beach attendance forecastingXGBoost modellingvideo-derived beach user dataweather and tidal influencescoastal safety management |
| spellingShingle | Bruno Castelle David Carayon Jeoffrey Dehez Sylvain Liquet Vincent Marieu Nadia Sénéchal Sandrine Lyser Jean-Philippe Savy Stéphanie Barneix Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting Journal of Marine Science and Engineering beach attendance forecasting XGBoost modelling video-derived beach user data weather and tidal influences coastal safety management |
| title | Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting |
| title_full | Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting |
| title_fullStr | Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting |
| title_full_unstemmed | Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting |
| title_short | Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting |
| title_sort | machine learning beach attendance forecast modelling from automatic video derived counting |
| topic | beach attendance forecasting XGBoost modelling video-derived beach user data weather and tidal influences coastal safety management |
| url | https://www.mdpi.com/2077-1312/13/6/1181 |
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