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Sea level forecasting using deep recurrent neural networks with high-resolution hydrodynamic model

author
statement of authorship
Saeed Rajabi-Kiasari, Artu Ellmann, Nicole Delpeche-Ellmann
source
publisher
journal volume number month
vol. 157
year of publication
pages
art. 104496
url
ISSN
0141-1187
1879-1549
notes
Includes bibliogr
Open Access
Open Access
scientific publication
teaduspublikatsioon
language
English
subject term
subject of location
keyword
sea level forecasting
LSTM
Long Short-Term Memory Networks (LSTMs)
GRU
Gated Recurrent Unit (GRU)
Baltic Sea
deep learning (DL)
recurrent neural networks (RNN)
spatiotemporal prediction
Gulf of Finland
hydro-geodesy
classifier
1.1
Scopus
scopus
Rajabi-Kiasari, S., Ellmann, A., Delpeche-Ellmann, N. Sea level forecasting using deep recurrent neural networks with high-resolution hydrodynamic model // Applied ocean research (2025) vol. 157, art. 104496. https://doi.org/10.1016/j.apor.2025.104496