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

Master thesis (2023)

Authors

P.A.K. van Asselt Civil Engineering & Geosciences

Contributors

José A. Á. Antolínez Coastal Engineering - CEG (mentor)
A.J.H.M. Reniers Environmental Fluid Mechanics - CEG (mentor)
A. Heinlein Numerical Analysis - EEMCS (mentor)
Panagiotis Athanasiou Deltares (graduation committee member)
Robert McCall Deltares (graduation committee member)
Faculty
Civil Engineering & Geosciences, Civil Engineering & Geosciences
Copyright: © 2023 Koen van Asselt
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Published Date

21-06-2023

Language

English

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Faculty

Civil Engineering & Geosciences

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.

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