Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 11, issue 6
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.
https://doi.org/10.5194/esurf-11-1145-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 6
Research article
 | 
13 Nov 2023
Research article |  | 13 Nov 2023

On the use of convolutional deep learning to predict shoreline change

Eduardo Gomez-de la Peña, Giovanni Coco, Colin Whittaker, and Jennifer Montaño

Viewed

Total article views: 2,319 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,825 427 67 2,319 50 41
  • HTML: 1,825
  • PDF: 427
  • XML: 67
  • Total: 2,319
  • BibTeX: 50
  • EndNote: 41
Views and downloads (calculated since 13 Jun 2023)
Cumulative views and downloads (calculated since 13 Jun 2023)

Viewed (geographical distribution)

Total article views: 2,319 (including HTML, PDF, and XML) Thereof 2,244 with geography defined and 75 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Apr 2025
Download
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.
Read more
Share