Articles | Volume 22, issue 12
https://doi.org/10.5194/nhess-22-3897-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-3897-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating dune erosion at the regional scale using a meta-model based on neural networks
Panagiotis Athanasiou
CORRESPONDING AUTHOR
Deltares, Delft, the Netherlands
Water Engineering and Management, Faculty of Engineering Technology,
University of Twente, Enschede, the Netherlands
Ap van Dongeren
Deltares, Delft, the Netherlands
Department of Coastal and Urban Risk & Resilience, IHE Delft
Institute for Water Education, Delft, the Netherlands
Alessio Giardino
Water Sector Group, Sustainable Development and Climate Change
Department, Asian Development Bank, Manila, Philippines
Michalis Vousdoukas
Joint Research Centre (JRC), European Commission, Seville, Spain
Jose A. A. Antolinez
Department of Hydraulic Engineering, Faculty of Civil Engineering and
Geosciences, Delft University of Technology, Delft, the Netherlands
Roshanka Ranasinghe
Deltares, Delft, the Netherlands
Water Engineering and Management, Faculty of Engineering Technology,
University of Twente, Enschede, the Netherlands
Department of Coastal and Urban Risk & Resilience, IHE Delft
Institute for Water Education, Delft, the Netherlands
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Cited
13 citations as recorded by crossref.
- A Comparative Analysis of In-Situ Wave Measurements and Reanalysis Models for Predicting Coastline Evolution: A Case Study of IJmuiden, The Netherlands J. Pais-Barbosa et al. https://doi.org/10.3390/w17071091
- A Review of Assessment Methods for Coastal Hydro-Environmental Processes: Research Trends and Challenges Q. Lee et al. https://doi.org/10.3390/w17223278
- Wave-driven storm impacts on Scotland's sedimentary barriers intensify with climate change N. Horsburgh et al. https://doi.org/10.1016/j.geomorph.2026.110489
- Data-driven modelling of coastal storm erosion for real-time forecasting at a wave-dominated embayed beach R. Ibaceta & M. Harley https://doi.org/10.1016/j.coastaleng.2024.104596
- A new approach for the assessment of coastal flooding risk. Application in Rhodes island, Greece D. Malliouri et al. https://doi.org/10.1016/j.apor.2024.104006
- An assessment of the impact of boundary conditions in dynamical downscaling techniques for fetch-limited waves A. Ruju & F. Viola https://doi.org/10.1080/21664250.2024.2399393
- Physics-based modeling of climate change impact on hurricane-induced coastal erosion hazards M. Jamous et al. https://doi.org/10.1038/s41612-023-00416-0
- Estimating nearshore infragravity wave conditions at large spatial scales T. Leijnse et al. https://doi.org/10.3389/fmars.2024.1355095
- Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives M. Abouhalima et al. https://doi.org/10.3390/jmse12040638
- Harnessing artificial neural networks for coastal erosion prediction: A systematic review A. Khan et al. https://doi.org/10.1016/j.marpol.2025.106704
- The importance of waves in large-scale coastal compound flooding: A case study of Hurricane Florence (2018) T. Leijnse et al. https://doi.org/10.1016/j.coastaleng.2025.104726
- Effects of storm surge exceedance value on overwash and inundation forecasts M. Palmsten et al. https://doi.org/10.1007/s11069-026-08184-9
- Predicting the response of complex systems for coastal management G. Hendrickx et al. https://doi.org/10.1016/j.coastaleng.2023.104289
13 citations as recorded by crossref.
- A Comparative Analysis of In-Situ Wave Measurements and Reanalysis Models for Predicting Coastline Evolution: A Case Study of IJmuiden, The Netherlands J. Pais-Barbosa et al. https://doi.org/10.3390/w17071091
- A Review of Assessment Methods for Coastal Hydro-Environmental Processes: Research Trends and Challenges Q. Lee et al. https://doi.org/10.3390/w17223278
- Wave-driven storm impacts on Scotland's sedimentary barriers intensify with climate change N. Horsburgh et al. https://doi.org/10.1016/j.geomorph.2026.110489
- Data-driven modelling of coastal storm erosion for real-time forecasting at a wave-dominated embayed beach R. Ibaceta & M. Harley https://doi.org/10.1016/j.coastaleng.2024.104596
- A new approach for the assessment of coastal flooding risk. Application in Rhodes island, Greece D. Malliouri et al. https://doi.org/10.1016/j.apor.2024.104006
- An assessment of the impact of boundary conditions in dynamical downscaling techniques for fetch-limited waves A. Ruju & F. Viola https://doi.org/10.1080/21664250.2024.2399393
- Physics-based modeling of climate change impact on hurricane-induced coastal erosion hazards M. Jamous et al. https://doi.org/10.1038/s41612-023-00416-0
- Estimating nearshore infragravity wave conditions at large spatial scales T. Leijnse et al. https://doi.org/10.3389/fmars.2024.1355095
- Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives M. Abouhalima et al. https://doi.org/10.3390/jmse12040638
- Harnessing artificial neural networks for coastal erosion prediction: A systematic review A. Khan et al. https://doi.org/10.1016/j.marpol.2025.106704
- The importance of waves in large-scale coastal compound flooding: A case study of Hurricane Florence (2018) T. Leijnse et al. https://doi.org/10.1016/j.coastaleng.2025.104726
- Effects of storm surge exceedance value on overwash and inundation forecasts M. Palmsten et al. https://doi.org/10.1007/s11069-026-08184-9
- Predicting the response of complex systems for coastal management G. Hendrickx et al. https://doi.org/10.1016/j.coastaleng.2023.104289
Saved (final revised paper)
Latest update: 26 Aug 2026
Short summary
Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can efficiently predict dune erosion are critical for coastal zone management and early warning systems. Here we develop such a model for the Dutch coast based on machine learning techniques, allowing for dune erosion estimations in a matter of seconds relative to available computationally expensive models. Validation of the model against benchmark data and observations shows good agreement.
Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can...
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