Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Abstract

As climate-driven risks for the world's coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change and informed coastal management choices. Artificial Intelligence, especially deep learning, is a powerful technology that has been rapidly evolving over the last couple of decades and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of deep learning models were tested, one set relying on localized modelling outputs or localized data sources and one set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing. The second set of reduced-dependency models provides regression values greater than 0.84 and 0.76 for training and testing. Both model types require a running time of the order of minutes, compared to the several hours of running times of the hydrodynamic models.Our results highlight the potential of deep learning and statistical models for coastal applications.

Exploring Mega‐Nourishment Interventions Using Long Short‐Term Memory (LSTM) Models and the Sand Engine Surface MATLAB Framework

“…However, there are significant uncertainties and challenges in relation to the morphological evolution of the coastline and evaluation of the effectiveness of different sand engines interventions. Artificial Intelligence (AI) can be an effective tool to address these challenges and offers promising solutions for comprehending and predicting complex coastlines dynamics (e.g., Kumar & Leonardi, 2023a, 2023b).…”