Machine learning for post-storm profile predictions
Using XBeach and convolutional neural network structure U-Net to predict 1D dune erosion profile shapes at the Holland Coast
P.A.K. van Asselt (TU Delft - Civil Engineering & Geosciences)
José A. Á. Antolinez (TU Delft - Coastal Engineering)
Adrianus J.H.M. Reniers (Environmental Fluid Mechanics)
Alexander Heinlein (TU Delft - Numerical Analysis)
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Abstract
To reduce computational efforts, surrogate models have been developed for dune erosion prediction. Current surrogate models can describe the relationship between the XBeach input and output (Athanasiou, 2022) and provides a prediction of a morphological indicator based on a parameterized input (profile shape parameters and hydrodynamics). In this research, first steps are taken to set-up a surrogate model that is able to deal with spatial input and the prediction of actual profile shapes along the Holland Coast. Using a convolutional neural network structure (U-Net), several model set-ups are explored and scaled-up to a realistic storm scenario along the Holland Coast.