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Preprints
https://doi.org/10.5194/egusphere-2023-958
https://doi.org/10.5194/egusphere-2023-958
13 Jun 2023
 | 13 Jun 2023

On the use of Convolutional Deep Learning to predict shoreline change

Eduardo Gomez-de la Pena, Giovanni Coco, Colin Whittaker, and Jennifer Montano

Abstract. The process of shoreline change is inherently complex and reliable predictions of shoreline position remain a key challenge in coastal research. Predicting shoreline evolution could potentially benefit from Deep Learning (DL), which is a recently developed and widely successful data-driven methodology. However, so far its implementation for shoreline time series data has been limited. The aim of this contribution is to investigate the potential of DL algorithms to predict interannual shoreline position derived from camera system observations at a New Zealand study site. We investigate the application of Convolutional Neural Networks (CNNs) and hybrid CNN - Long Short-Term Memory networks. We compare our results with two established models, a shoreline equilibrium model, and a model that addresses time scales in shoreline drivers. Using a systematic search and different measures of fitness we found DL models that outperformed the reference models when simulating the variability and distribution of the observations. Overall, these results indicate that DL models have potential to improve accuracy and reliability over current models.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint

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
Earth Surf. Dynam., 11, 1145–1160, https://doi.org/10.5194/esurf-11-1145-2023,https://doi.org/10.5194/esurf-11-1145-2023, 2023
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Predicting how shorelines change over time is a major challenge in coastal research. We here...
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