Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-605-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-605-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Feb 2025
Model description paper |
| 04 Feb 2025
| 04 Feb 2025
HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures
Marko Rus
CORRESPONDING AUTHOR
Office for Meteorology, Hydrology and Oceanography, Slovenian Environment Agency, Ljubljana, Slovenia
Visual Cognitive Systems Lab, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Hrvoje Mihanović
Physical Oceanography Laboratory, Institute of Oceanography and Fisheries, Split, Croatia
Office for Meteorology, Hydrology and Oceanography, Slovenian Environment Agency, Ljubljana, Slovenia
Marine Biology Station, National Institute of Biology, Piran, Slovenia
Matej Kristan
Visual Cognitive Systems Lab, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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Short summary
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant...
