Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast DOI Creative Commons
Maria Emanuela Mihailov, Alecsandru Vladimir Chiroşca, Gianina Chiroşca

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(2), С. 199 - 199

Опубликована: Янв. 22, 2025

This study explores the use of Temporal Fusion Transformers (TFTs), an AI/ML technique, to enhance prediction coastal dynamics along Western Black Sea coast. We integrate in-situ observations from five meteo-oceanographic stations with modelled geospatial marine data Copernicus Marine Service. TFTs are employed refine predictions shallow water by considering atmospheric influences, a particular focus on wave-wind correlations in regions. Atmospheric pressure and temperature treated as latitude-dependent constants, specific investigations into extreme events like freezing solar radiation-induced turbulence. Explainable AI (XAI) is exploited ensure transparent model interpretations identify key influential input variables. Data attribution strategies address missing concerns, while ensemble modelling enhances overall robustness. The models demonstrate significant improvement accuracy compared traditional methods. research provides deeper understanding atmosphere-marine interactions demonstrates efficacy Artificial intelligence (AI)/Machine Learning (ML) bridging observational gaps for informed zone management decisions, essential maritime safety

Язык: Английский

Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast DOI Creative Commons
Maria Emanuela Mihailov, Alecsandru Vladimir Chiroşca, Gianina Chiroşca

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(2), С. 199 - 199

Опубликована: Янв. 22, 2025

This study explores the use of Temporal Fusion Transformers (TFTs), an AI/ML technique, to enhance prediction coastal dynamics along Western Black Sea coast. We integrate in-situ observations from five meteo-oceanographic stations with modelled geospatial marine data Copernicus Marine Service. TFTs are employed refine predictions shallow water by considering atmospheric influences, a particular focus on wave-wind correlations in regions. Atmospheric pressure and temperature treated as latitude-dependent constants, specific investigations into extreme events like freezing solar radiation-induced turbulence. Explainable AI (XAI) is exploited ensure transparent model interpretations identify key influential input variables. Data attribution strategies address missing concerns, while ensemble modelling enhances overall robustness. The models demonstrate significant improvement accuracy compared traditional methods. research provides deeper understanding atmosphere-marine interactions demonstrates efficacy Artificial intelligence (AI)/Machine Learning (ML) bridging observational gaps for informed zone management decisions, essential maritime safety

Язык: Английский

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