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.

Eduardo Gomez-de la Pena et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-958', Andres Payo, 31 Jul 2023
    • AC1: 'Reply on RC1', Eduardo Gomez- de la Pena, 23 Aug 2023
  • RC2: 'Comment on egusphere-2023-958', Anonymous Referee #2, 08 Aug 2023
    • AC2: 'Reply on RC2', Eduardo Gomez- de la Pena, 23 Aug 2023

Eduardo Gomez-de la Pena et al.

Eduardo Gomez-de la Pena et al.


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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 modeling 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.