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)
Author(s)

P.A.K. van Asselt (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

José A.Á. Antolínez – Mentor (TU Delft - Coastal Engineering)

Ad Reniers – Mentor (TU Delft - Environmental Fluid Mechanics)

Alexander Heinlein – Mentor (TU Delft - Numerical Analysis)

Panagiotis Athanasiou – Graduation committee member (Deltares)

R. T. McCall – Graduation committee member (Deltares)

Faculty
Civil Engineering & Geosciences
Copyright
© 2023 Koen van Asselt
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Koen van Asselt
Graduation Date
21-06-2023
Awarding Institution
Delft University of Technology
Programme
Civil Engineering | Hydraulic Engineering
Faculty
Civil Engineering & Geosciences
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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.

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