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.