Articles | Volume 11, issue 6
https://doi.org/10.5194/esurf-11-1145-2023
https://doi.org/10.5194/esurf-11-1145-2023
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

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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.