Data-driven modelling and AI frontiers in coastal morphodynamic predictions
Predicting coastal morphodynamics is a challenging task due to the complexity of process dynamics and the vast time and space scales involved. Numerous approaches including process-based numerical models, equilibrium or behavioural models, and data-driven models have been widely used for predictions, depending on the time and space scale of interest, the purpose of the prediction, and the availability of resources and expertise. When reliable and long term field measurements are available, morphodynamic predictions can be done by identifying historic trends of variability and extrapolating those into the future. In this talk, some data-driven modelling techniques widely used in the literature and recent developments of AI-based data-driven prediction approaches will be discussed.
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