Design optimization for flood defenses using machine learning
L.C. Guichelaar (TU Delft - Civil Engineering & Geosciences)
Ronald Brinkgreve – Mentor (TU Delft - Geo-engineering)
Guillaume Rongier – Graduation committee member (TU Delft - Applied Geology)
Juan Pablo Aguilar Lopez – Graduation committee member (TU Delft - Hydraulic Structures and Flood Risk)
R.A. van der Eijk – Graduation committee member (TU Delft - Geo-engineering)
Koen de Jong – Graduation committee member (Witteveen+Bos)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.