Design optimization for flood defenses using machine learning

Master thesis (2024)

Authors

L.C. Guichelaar Civil Engineering & Geosciences

Contributors

G. Rongier Applied Geology - CEG (graduation committee member)
J.P. Aguilar Lopez Hydraulic Structures and Flood Risk - CEG (graduation committee member)
R.A. van der Eijk (graduation committee member)
Koen de Jong Witteveen+Bos (graduation committee member)
Faculty
Civil Engineering & Geosciences
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Published Date

30-08-2024

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

This thesis aims to find the best way to construct a surrogate model for the inner slope stability of a dike and combine this surrogate model with other (machine learning) models to generate conceptual flood defenses, making it possible to optimize the dike design using an interdisciplinary MCA.

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