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
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Cited
25 citations as recorded by crossref.
- Spatially aware deep learning and explainable AI reveal geomorphic control hierarchy in a monsoon-dominated coastal lagoon M. Mre et al. https://doi.org/10.1016/j.rineng.2026.111316
- Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter https://doi.org/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. https://doi.org/10.1016/j.envsoft.2025.106624
- Machine learning study of shoreline change in Western and Southwestern coastlines of Sri Lanka H. Dananjaya et al. https://doi.org/10.1680/jmaen.25.00028
- Shorelines as graphs: A spatio-temporal data-driven model for predicting shoreline dynamics Y. Mao & K. Splinter https://doi.org/10.1016/j.coastaleng.2026.105017
- Benchmarking shoreline prediction models over multi-decadal timescales Y. Mao et al. https://doi.org/10.1038/s43247-025-02550-4
- Predicting coastal variations in non-storm conditions with machine learning A. Jabari et al. https://doi.org/10.1515/geo-2025-0770
- Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast M. Mihailov et al. https://doi.org/10.3390/jmse13020199
- A mixture of experts approach to sandy shoreline modelling in storm dominated systems K. Calcraft et al. https://doi.org/10.1016/j.coastaleng.2025.104813
- Spatio-Temporal Shoreline Changes and AI-Based Predictions for Sustainable Management of the Damietta–Port Said Coast, Nile Delta, Egypt H. El-Asmar et al. https://doi.org/10.3390/su18031557
- Development of a ConvLSTM-Net Model for Coastal Erosion Hazard Prediction Based on Spatiotemporal Data W. Boen et al. https://doi.org/10.48084/etasr.17951
- Aiding sea turtle conservation through coastal management J. Christiaanse et al. https://doi.org/10.3389/fmars.2025.1669885
- A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting D. Di Luccio et al. https://doi.org/10.1007/s11227-024-06188-5
- Comparative assessment of AI-based and classical DSAS approaches in multi-temporal shoreline prediction: A case study of Ras El-Bar coast, Egypt H. El-Asmar & M. Felfla https://doi.org/10.1016/j.isprsjprs.2026.01.040
- Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models K. Calcraft et al. https://doi.org/10.1016/j.envsoft.2024.106236
- Harnessing artificial neural networks for coastal erosion prediction: A systematic review A. Khan et al. https://doi.org/10.1016/j.marpol.2025.106704
- Predicting shoreline changes using deep learning techniques with Bayesian optimisation T. Manamperi et al. https://doi.org/10.1016/j.coastaleng.2025.104856
- Deep Learning-Driven Sandy Beach Resilience Assessment: Integrating External Forcing Forecasting, Process Simulation, and Risk-Informed Decision Support Y. Jiang et al. https://doi.org/10.3390/w17233383
- Accuracy Assessment of Shoreline Extraction Using MLS Data from a USV and UAV Orthophoto on a Complex Inland Lake M. Specht & O. Specht https://doi.org/10.3390/rs17243940
- Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach O. Repina et al. https://doi.org/10.1016/j.coastaleng.2025.104738
- A temporal-enhanced Segment Anything Model for precise and robust shoreline segmentation G. Chen et al. https://doi.org/10.1016/j.engappai.2026.115480
- A systematic review of integrated remote sensing and low-cost technologies for data-driven coastal monitoring H. Riaz & S. Gharbia https://doi.org/10.1016/j.ocecoaman.2026.108231
- CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences P. Zhang et al. https://doi.org/10.1080/17538947.2025.2513591
- EnCo SupCon: Entropy-driven supervised contrastive learning for discriminative feature representations from remote sensing imagery K. Bannigan & S. Henna https://doi.org/10.1016/j.rineng.2025.108311
- BDCN_UNet: Advanced shoreline extraction techniques integrating deep learning A. Mahmoud et al. https://doi.org/10.1007/s12145-024-01693-w
25 citations as recorded by crossref.
- Spatially aware deep learning and explainable AI reveal geomorphic control hierarchy in a monsoon-dominated coastal lagoon M. Mre et al. https://doi.org/10.1016/j.rineng.2026.111316
- Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter https://doi.org/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. https://doi.org/10.1016/j.envsoft.2025.106624
- Machine learning study of shoreline change in Western and Southwestern coastlines of Sri Lanka H. Dananjaya et al. https://doi.org/10.1680/jmaen.25.00028
- Shorelines as graphs: A spatio-temporal data-driven model for predicting shoreline dynamics Y. Mao & K. Splinter https://doi.org/10.1016/j.coastaleng.2026.105017
- Benchmarking shoreline prediction models over multi-decadal timescales Y. Mao et al. https://doi.org/10.1038/s43247-025-02550-4
- Predicting coastal variations in non-storm conditions with machine learning A. Jabari et al. https://doi.org/10.1515/geo-2025-0770
- Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction Along the Western Black Sea Coast M. Mihailov et al. https://doi.org/10.3390/jmse13020199
- A mixture of experts approach to sandy shoreline modelling in storm dominated systems K. Calcraft et al. https://doi.org/10.1016/j.coastaleng.2025.104813
- Spatio-Temporal Shoreline Changes and AI-Based Predictions for Sustainable Management of the Damietta–Port Said Coast, Nile Delta, Egypt H. El-Asmar et al. https://doi.org/10.3390/su18031557
- Development of a ConvLSTM-Net Model for Coastal Erosion Hazard Prediction Based on Spatiotemporal Data W. Boen et al. https://doi.org/10.48084/etasr.17951
- Aiding sea turtle conservation through coastal management J. Christiaanse et al. https://doi.org/10.3389/fmars.2025.1669885
- A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting D. Di Luccio et al. https://doi.org/10.1007/s11227-024-06188-5
- Comparative assessment of AI-based and classical DSAS approaches in multi-temporal shoreline prediction: A case study of Ras El-Bar coast, Egypt H. El-Asmar & M. Felfla https://doi.org/10.1016/j.isprsjprs.2026.01.040
- Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models K. Calcraft et al. https://doi.org/10.1016/j.envsoft.2024.106236
- Harnessing artificial neural networks for coastal erosion prediction: A systematic review A. Khan et al. https://doi.org/10.1016/j.marpol.2025.106704
- Predicting shoreline changes using deep learning techniques with Bayesian optimisation T. Manamperi et al. https://doi.org/10.1016/j.coastaleng.2025.104856
- Deep Learning-Driven Sandy Beach Resilience Assessment: Integrating External Forcing Forecasting, Process Simulation, and Risk-Informed Decision Support Y. Jiang et al. https://doi.org/10.3390/w17233383
- Accuracy Assessment of Shoreline Extraction Using MLS Data from a USV and UAV Orthophoto on a Complex Inland Lake M. Specht & O. Specht https://doi.org/10.3390/rs17243940
- Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach O. Repina et al. https://doi.org/10.1016/j.coastaleng.2025.104738
- A temporal-enhanced Segment Anything Model for precise and robust shoreline segmentation G. Chen et al. https://doi.org/10.1016/j.engappai.2026.115480
- A systematic review of integrated remote sensing and low-cost technologies for data-driven coastal monitoring H. Riaz & S. Gharbia https://doi.org/10.1016/j.ocecoaman.2026.108231
- CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences P. Zhang et al. https://doi.org/10.1080/17538947.2025.2513591
- EnCo SupCon: Entropy-driven supervised contrastive learning for discriminative feature representations from remote sensing imagery K. Bannigan & S. Henna https://doi.org/10.1016/j.rineng.2025.108311
- BDCN_UNet: Advanced shoreline extraction techniques integrating deep learning A. Mahmoud et al. https://doi.org/10.1007/s12145-024-01693-w
Saved (final revised paper)
Latest update: 23 Jul 2026
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...
