Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-271-2023
© Author(s) 2023. 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-16-271-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
10 Jan 2023
Model description paper |
| 10 Jan 2023
| 10 Jan 2023
HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
Marko Rus
CORRESPONDING AUTHOR
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Anja Fettich
Slovenian Environment Agency, Office for Meteorology, Hydrology and Oceanography, Ljubljana, Slovenia
Matej Kristan
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Matjaž Ličer
Slovenian Environment Agency, Office for Meteorology, Hydrology and Oceanography, Ljubljana, Slovenia
National Institute of Biology, Marine Biology Station, Piran, Slovenia
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Cited
18 citations as recorded by crossref.
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al.
- Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea A. Barzandeh et al.
- How to disentangle sea-level rise and a number of other processes influencing coastal floods? M. Orlić & M. Pasarić
- An Autoencoder-Based Framework for Multimodal Fusion in Forecasting Tropical Cyclone-Induced Sea Surface Height Responses H. Cui et al.
- Review of machine learning methods for sea level change modeling and prediction A. Ayinde et al.
- Modeling regional mean sea level based on climate measurements using a stacked ensemble approach M. Elnabwy et al.
- Coastal Flood Risk Assessment: An Approach to Accurately Map Flooding through National Registry-Reported Events E. Kralj et al.
- A computer vision approach to estimate the localized sea state A. Vorkapic et al.
- Predicting Seiche-Impacted Estuarine Water Levels with Machine Learning Methods N. Guillou
- DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin P. Mlakar et al.
- Catalogue of extreme sea levels recorded at tide-gauge station Bakar in the northeastern Adriatic Sea (Mediterranean) I. Međugorac et al.
- HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data M. Rus et al.
- Exploring LSTM neural networks for sea level forecasting based on two-decadal tidal and meteorological data N. Papadopoulos & V. Gikas
- Drivers of high-frequency extreme sea levels around northern Europe – synergies between recurrent neural networks and random forest C. Heuzé et al.
- Investigation of Physics-Informed Methods for Improving Sea Surface Height Prediction Based on Neural Networks in the South China Sea L. Huang et al.
- Gradient-informed data splitting and model setup for machine learning prediction of storm surge Y. Wang et al.
- SeaQC-X: Transferability of a machine learning-based sea level quality control framework F. Bauer et al.
- HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures M. Rus et al.
18 citations as recorded by crossref.
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al.
- Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea A. Barzandeh et al.
- How to disentangle sea-level rise and a number of other processes influencing coastal floods? M. Orlić & M. Pasarić
- An Autoencoder-Based Framework for Multimodal Fusion in Forecasting Tropical Cyclone-Induced Sea Surface Height Responses H. Cui et al.
- Review of machine learning methods for sea level change modeling and prediction A. Ayinde et al.
- Modeling regional mean sea level based on climate measurements using a stacked ensemble approach M. Elnabwy et al.
- Coastal Flood Risk Assessment: An Approach to Accurately Map Flooding through National Registry-Reported Events E. Kralj et al.
- A computer vision approach to estimate the localized sea state A. Vorkapic et al.
- Predicting Seiche-Impacted Estuarine Water Levels with Machine Learning Methods N. Guillou
- DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin P. Mlakar et al.
- Catalogue of extreme sea levels recorded at tide-gauge station Bakar in the northeastern Adriatic Sea (Mediterranean) I. Međugorac et al.
- HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data M. Rus et al.
- Exploring LSTM neural networks for sea level forecasting based on two-decadal tidal and meteorological data N. Papadopoulos & V. Gikas
- Drivers of high-frequency extreme sea levels around northern Europe – synergies between recurrent neural networks and random forest C. Heuzé et al.
- Investigation of Physics-Informed Methods for Improving Sea Surface Height Prediction Based on Neural Networks in the South China Sea L. Huang et al.
- Gradient-informed data splitting and model setup for machine learning prediction of storm surge Y. Wang et al.
- SeaQC-X: Transferability of a machine learning-based sea level quality control framework F. Bauer et al.
- HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures M. Rus et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm...
