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.
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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EGUsphere, https://doi.org/10.5194/egusphere-2025-2292, https://doi.org/10.5194/egusphere-2025-2292, 2025
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This study evaluates the performance of an early warning system for coastal flooding operating at a beach scale. The system is found to effectively capture total water level exceedances based on predefined morphological thresholds and trigger timely warnings, particularly under energetic sea conditions. Its forecasts are found to align well with selected overwash/flood events of varying magnitude and duration, captured by an on-site coastal video monitoring station.
Tate Kimpton, Colin Whittaker, Pablo Higuera, and Liam Wotherspoon
EGUsphere, https://doi.org/10.5194/egusphere-2024-3724, https://doi.org/10.5194/egusphere-2024-3724, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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This research assesses tsunami exposure across New Zealand using detailed inundation maps for various tsunami scenarios. An efficient and accurate model highlights both urban centres and provincial regions as highly exposed, with significant impacts on buildings, infrastructure, and land. The findings provide critical understanding to help communities and decision-makers better plan for tsunamis, offering valuable insights for improving resilience and protecting assets nationwide.
Wagner L. L. Costa, Karin R. Bryan, and Giovanni Coco
Nat. Hazards Earth Syst. Sci., 23, 3125–3146, https://doi.org/10.5194/nhess-23-3125-2023, https://doi.org/10.5194/nhess-23-3125-2023, 2023
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For predicting flooding events at the coast, topo-bathymetric data are essential. However, elevation data can be unavailable. To tackle this issue, recent efforts have centred on the use of satellite-derived topography (SDT) and bathymetry (SDB). This work is aimed at evaluating their accuracy and use for flooding prediction in enclosed estuaries. Results show that the use of SDT and SDB in numerical modelling can produce similar predictions when compared to the surveyed elevation data.
Charline Dalinghaus, Giovanni Coco, and Pablo Higuera
Nat. Hazards Earth Syst. Sci., 23, 2157–2169, https://doi.org/10.5194/nhess-23-2157-2023, https://doi.org/10.5194/nhess-23-2157-2023, 2023
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Wave setup is a critical component of coastal flooding. Consequently, understanding and being able to predict wave setup is vital to protect coastal resources and the population living near the shore. Here, we applied machine learning to improve the accuracy of present predictors of wave setup. The results show that the new predictors outperform existing formulas demonstrating the capability of machine learning models to provide a physically sound description of wave setup.
Matthew W. Hayward, Emily M. Lane, Colin N. Whittaker, Graham S. Leonard, and William L. Power
Nat. Hazards Earth Syst. Sci., 23, 955–971, https://doi.org/10.5194/nhess-23-955-2023, https://doi.org/10.5194/nhess-23-955-2023, 2023
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In this paper, 20 explosive volcanic eruption scenarios of differing location and magnitude are simulated to investigate tsunami generation in Lake Taupō, New Zealand. A non-hydrostatic multilayer numerical scheme resolves the highly dispersive generated wavefield. Inundation, hydrographic and related hazard outputs are produced, indicating that significant inundation around the lake shore begins above 5 on the volcanic explosivity index.
Matthew W. Hayward, Colin N. Whittaker, Emily M. Lane, William L. Power, Stéphane Popinet, and James D. L. White
Nat. Hazards Earth Syst. Sci., 22, 617–637, https://doi.org/10.5194/nhess-22-617-2022, https://doi.org/10.5194/nhess-22-617-2022, 2022
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Volcanic eruptions can produce tsunamis through multiple mechanisms. We present validation cases for a numerical method used in simulating waves caused by submarine explosions: a laboratory flume experiment and waves generated by explosions at field scale. We then demonstrate the use of the scheme for simulating analogous volcanic eruptions, illustrating the resulting wavefield. We show that this scheme models such dispersive sources more proficiently than standard tsunami models.
Yizhang Wei, Yining Chen, Jufei Qiu, Zeng Zhou, Peng Yao, Qin Jiang, Zheng Gong, Giovanni Coco, Ian Townend, and Changkuan Zhang
Earth Surf. Dynam., 10, 65–80, https://doi.org/10.5194/esurf-10-65-2022, https://doi.org/10.5194/esurf-10-65-2022, 2022
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The barrier tidal basin is increasingly altered by human activity and sea-level rise. These environmental changes probably lead to the emergence or disappearance of islands, yet the effect of rocky islands on the evolution of tidal basins remains poorly investigated. Using numerical experiments, we explore the evolution of tidal basins under varying numbers and locations of islands. This work provides insights for predicting the response of barrier tidal basins in a changing environment.
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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...