Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-2057-2021
© Author(s) 2021. 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-14-2057-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 Apr 2021
Model description paper |
| 21 Apr 2021
| 21 Apr 2021
HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic
Lojze Žust
CORRESPONDING AUTHOR
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Anja Fettich
Slovenian Environment Agency, Group for Meteorological, Hydrological and Oceanographic Modelling, Ljubljana, Slovenia
Matej Kristan
Faculty of Computer and Information Science, Visual Cognitive Systems Lab, University of Ljubljana, Ljubljana, Slovenia
Matjaž Ličer
National Institute of Biology, Marine Biology Station, Piran, Slovenia
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- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. 10.3390/jmse11091729
- Convolutional Neural Network and Optical Flow for the Assessment of Wave and Tide Parameters from Video Analysis (LEUCOTEA): An Innovative Tool for Coastal Monitoring G. Scardino et al. 10.3390/rs14132994
- HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic M. Rus et al. 10.5194/gmd-16-271-2023
- Forecasting of absolute dynamic topography using deep learning algorithm with application to the Baltic Sea S. Rajabi-Kiasari et al. 10.1016/j.cageo.2023.105406
- Review of machine learning methods for sea level change modeling and prediction A. Ayinde et al. 10.1016/j.scitotenv.2024.176410
- Coastal sea level monitoring in the Mediterranean and Black seas B. Pérez Gómez et al. 10.5194/os-18-997-2022
- DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin P. Mlakar et al. 10.5194/gmd-17-4705-2024
- Predicting Sea Level Rise Using Artificial Intelligence: A Review N. Bahari et al. 10.1007/s11831-023-09934-9
- A deep-learning model for rapid spatiotemporal prediction of coastal water levels A. Shahabi & N. Tahvildari 10.1016/j.coastaleng.2024.104504
- Coastal Flood Risk Assessment: An Approach to Accurately Map Flooding through National Registry-Reported Events E. Kralj et al. 10.3390/jmse11122290
- Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach A. Yavuzdoğan & E. Tanir Kayıkçı 10.1016/j.ocemod.2024.102463
- Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. 10.3390/jmse11112154
- Prepletanje umetne inteligence in fizike pri napovedovanju obalnih poplav M. Ličer et al. 10.3986/alternator.2021.35
- A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields I. Mulia et al. 10.1038/s41598-023-35093-9
- Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System H. Guo et al. 10.3389/fmars.2021.804541
14 citations as recorded by crossref.
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. 10.3390/jmse11091729
- Convolutional Neural Network and Optical Flow for the Assessment of Wave and Tide Parameters from Video Analysis (LEUCOTEA): An Innovative Tool for Coastal Monitoring G. Scardino et al. 10.3390/rs14132994
- HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic M. Rus et al. 10.5194/gmd-16-271-2023
- Forecasting of absolute dynamic topography using deep learning algorithm with application to the Baltic Sea S. Rajabi-Kiasari et al. 10.1016/j.cageo.2023.105406
- Review of machine learning methods for sea level change modeling and prediction A. Ayinde et al. 10.1016/j.scitotenv.2024.176410
- Coastal sea level monitoring in the Mediterranean and Black seas B. Pérez Gómez et al. 10.5194/os-18-997-2022
- DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin P. Mlakar et al. 10.5194/gmd-17-4705-2024
- Predicting Sea Level Rise Using Artificial Intelligence: A Review N. Bahari et al. 10.1007/s11831-023-09934-9
- A deep-learning model for rapid spatiotemporal prediction of coastal water levels A. Shahabi & N. Tahvildari 10.1016/j.coastaleng.2024.104504
- Coastal Flood Risk Assessment: An Approach to Accurately Map Flooding through National Registry-Reported Events E. Kralj et al. 10.3390/jmse11122290
- Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach A. Yavuzdoğan & E. Tanir Kayıkçı 10.1016/j.ocemod.2024.102463
- Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. 10.3390/jmse11112154
- Prepletanje umetne inteligence in fizike pri napovedovanju obalnih poplav M. Ličer et al. 10.3986/alternator.2021.35
- A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields I. Mulia et al. 10.1038/s41598-023-35093-9
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
Adriatic basin sea level modelling is a challenging problem due to the interplay between terrain, weather, tides and seiches. Current state-of-the-art numerical models (e.g. NEMO) require large computational resources to produce reliable forecasts. In this study we propose HIDRA, a novel deep learning approach for sea level modeling, which drastically reduces the numerical cost while demonstrating predictive capabilities comparable to that of the NEMO model, outperforming it in many instances.
Adriatic basin sea level modelling is a challenging problem due to the interplay between...
