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.

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.

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.

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.

Sandbars are ubiquitous features of the surf zone. They are rarely straight and often develop crescentic shapes. Double sandbar systems are also common, but the possibility of feedback between inner and outer sandbars has not been fully explored. The presence of double sandbar systems affects wave transformation and can result in a variety of spatial patterns. Here we model the conditions, waves and initial bathymetry that lead to the emergence of different patterns.