Articles | Volume 11, issue 6
https://doi.org/10.5194/esurf-11-1145-2023
© 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/esurf-11-1145-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2023
Research article |
| 13 Nov 2023
| 13 Nov 2023
On the use of convolutional deep learning to predict shoreline change
Eduardo Gomez-de la Peña
CORRESPONDING AUTHOR
School of Environment, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Giovanni Coco
School of Environment, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Colin Whittaker
Department of Civil and Environmental Engineering, The University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Jennifer Montaño
Auckland Council – Air, Land, and Biodiversity Team, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
Viewed
Total article views: 3,040 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Jun 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,432 | 535 | 73 | 3,040 | 83 | 94 |
- HTML: 2,432
- PDF: 535
- XML: 73
- Total: 3,040
- BibTeX: 83
- EndNote: 94
Total article views: 2,400 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Nov 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,025 | 326 | 49 | 2,400 | 68 | 83 |
- HTML: 2,025
- PDF: 326
- XML: 49
- Total: 2,400
- BibTeX: 68
- EndNote: 83
Total article views: 640 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Jun 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 407 | 209 | 24 | 640 | 15 | 11 |
- HTML: 407
- PDF: 209
- XML: 24
- Total: 640
- BibTeX: 15
- EndNote: 11
Viewed (geographical distribution)
Total article views: 3,040 (including HTML, PDF, and XML)
Thereof 2,956 with geography defined
and 84 with unknown origin.
Total article views: 2,400 (including HTML, PDF, and XML)
Thereof 2,318 with geography defined
and 82 with unknown origin.
Total article views: 640 (including HTML, PDF, and XML)
Thereof 638 with geography defined
and 2 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting D. Di Luccio et al. 10.1007/s11227-024-06188-5
- Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models K. Calcraft et al. 10.1016/j.envsoft.2024.106236
- Harnessing artificial neural networks for coastal erosion prediction: A systematic review A. Khan et al. 10.1016/j.marpol.2025.106704
- Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter 10.1016/j.coastaleng.2024.104685
- A coupling approach for long-term 3D morphological evolution of sandy coasts under sea-level rise M. Traboulsi et al. 10.1016/j.envsoft.2025.106624
- Benchmarking shoreline prediction models over multi-decadal timescales Y. Mao et al. 10.1038/s43247-025-02550-4
- Predicting coastal variations in non-storm conditions with machine learning A. Jabari et al. 10.1515/geo-2025-0770
- Predicting shoreline changes using deep learning techniques with Bayesian optimisation T. Manamperi et al. 10.1016/j.coastaleng.2025.104856
- Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast M. Mihailov et al. 10.3390/jmse13020199
- A mixture of experts approach to sandy shoreline modelling in storm dominated systems K. Calcraft et al. 10.1016/j.coastaleng.2025.104813
- Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach O. Repina et al. 10.1016/j.coastaleng.2025.104738
- CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences P. Zhang et al. 10.1080/17538947.2025.2513591
- Aiding sea turtle conservation through coastal management J. Christiaanse et al. 10.3389/fmars.2025.1669885
- BDCN_UNet: Advanced shoreline extraction techniques integrating deep learning A. Mahmoud et al. 10.1007/s12145-024-01693-w
- NON-STATIONARY PARAMETERS ON EQUILIBRIUM-BASED SHORELINE EVOLUTION MODELS C. Cardona et al. 10.9753/icce.v38.management.138
14 citations as recorded by crossref.
- A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting D. Di Luccio et al. 10.1007/s11227-024-06188-5
- Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models K. Calcraft et al. 10.1016/j.envsoft.2024.106236
- Harnessing artificial neural networks for coastal erosion prediction: A systematic review A. Khan et al. 10.1016/j.marpol.2025.106704
- Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter 10.1016/j.coastaleng.2024.104685
- A coupling approach for long-term 3D morphological evolution of sandy coasts under sea-level rise M. Traboulsi et al. 10.1016/j.envsoft.2025.106624
- Benchmarking shoreline prediction models over multi-decadal timescales Y. Mao et al. 10.1038/s43247-025-02550-4
- Predicting coastal variations in non-storm conditions with machine learning A. Jabari et al. 10.1515/geo-2025-0770
- Predicting shoreline changes using deep learning techniques with Bayesian optimisation T. Manamperi et al. 10.1016/j.coastaleng.2025.104856
- Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast M. Mihailov et al. 10.3390/jmse13020199
- A mixture of experts approach to sandy shoreline modelling in storm dominated systems K. Calcraft et al. 10.1016/j.coastaleng.2025.104813
- Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach O. Repina et al. 10.1016/j.coastaleng.2025.104738
- CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences P. Zhang et al. 10.1080/17538947.2025.2513591
- Aiding sea turtle conservation through coastal management J. Christiaanse et al. 10.3389/fmars.2025.1669885
- BDCN_UNet: Advanced shoreline extraction techniques integrating deep learning A. Mahmoud et al. 10.1007/s12145-024-01693-w
1 citations as recorded by crossref.
Latest update: 22 Oct 2025
Short summary
Predicting how shorelines change over time is a major challenge in coastal research. We here have turned to deep learning (DL), a data-driven modelling approach, to predict the movement of shorelines using observations from a camera system in New Zealand. The DL models here implemented succeeded in capturing the variability and distribution of the observed shoreline data. Overall, these findings indicate that DL has the potential to enhance the accuracy of current shoreline change predictions.
Predicting how shorelines change over time is a major challenge in coastal research. We here...
