| Title: | Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea |
|---|
| Authors: | ID Barzandeh, Amirhossein (Author)
ID Ličer, Matjaž (Author)
ID Rus, Marko (Author)
ID Kristan, Matej (Author)
ID Maljutenko, Ilja (Author)
ID Elken, Jüri (Author)
ID Lagemaa, Priidik (Author)
ID Uiboupin, Rivo (Author) |
|---|
| Files: |  URL - Source URL, visit https://os.copernicus.org/articles/21/1315/2025/
 PDF - Presentation file, download (6,41 MB)
MD5: E3EB03E25C15DDAB40B4FB182F0E8A65
|
|---|
| Language: | English |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: |  NIB - National Institute of Biology
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | sea flooding, deep learning, convolutional networks |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 14.07.2025 |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 1315–1327 |
|---|
| Numbering: | Vol. 21, issue 4 |
|---|
| PID: | 20.500.12556/DiRROS-23534  |
|---|
| UDC: | 574.5 |
|---|
| ISSN on article: | 1812-0792 |
|---|
| DOI: | 10.5194/os-21-1315-2025 |
|---|
| COBISS.SI-ID: | 243923971 |
|---|
| Note: | 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;
|
|---|
| Publication date in DiRROS: | 08.09.2025 |
|---|
| Views: | 356 |
|---|
| Downloads: | 152 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
