20.500.12556/DiRROS-23534 Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea Sea level predictions, typically derived from 3D hydrodynamic models, are computationally intensive and subject to uncertainties stemming from physical representation and inaccuracies in initial or boundary conditions. As a complementary alternative, data-driven machine learning models provide a computationally efficient solution with comparable accuracy. This study employs the deep-learning model HIDRA2 to forecast hourly sea levels at five coastal stations along the Estonian coastline of the Baltic Sea, evaluating its performance across various forecast lead times. Compared to the regional NEMOBAL and subregional NEMOEST hydrodynamic models, HIDRA2 frequently outperforms both, particularly in terms of overall forecast skill. While HIDRA2 shows limitations in resolving high-frequency sea level variability above (6h) 1, it effectively reproduces energy in lower-frequency bands below (18h) 1. Errors tend to average out over longer time windows encompassing multiple seiche periods, enabling HIDRA2 to surpass the overall performance of the NEMO models. These findings underscore HIDRA2’s potential as a robust, efficient, and reliable tool for operational sea level forecasting and coastal management in the eastern Baltic Sea region. sea flooding deep learning convolutional networks poplavljanje morja globoko učenje konvolucijske mreže true false true Angleški jezik Slovenski jezik Neznano 2025-09-08 12:39:02 2025-09-08 12:39:02 2025-10-24 03:54:31 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Soavtorji: Matjaž Ličer, Marko Rus, Matej Kristan, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, Rivo Uiboupin; Opis vira z dne 25. 7. 2025; str. 1315–1327 issue 4 Vol. 21 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-07-14 574.5 1812-0792 10.5194/os-21-1315-2025 243923971 https://os.copernicus.org/articles/21/1315/2025/ 1 0a8448e1-8ca0-11f0-9bb7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=34813 RAZ_Barzandeh_Amirhossein_2025.pdf RAZ_Barzandeh_Amirhossein_2025.pdf 1 E3EB03E25C15DDAB40B4FB182F0E8A65 67ff6e1410fe6e172c198e4c368c7969404b23c58b00a32026ab40ff8f38eaa0 24e6dcc2-8ca0-11f0-9bb7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=34814 Nacionalni inštitut za biologijo 0 0 0