Estimating dune erosion at the regional scale using a meta-model based on neural networks

Sandy beaches and dune systems have high recreational and ecological value, and they offer protection against flooding during storms. At the same time, these systems are very vulnerable to storm impacts. However, such models come with high computational costs, hindering their use in real-life applications which demand many simulations and/or involve a large spatial–temporal domain. Here we design a novel meta-model to predict dune erosion volume (DEV) at the Dutch coast, based on artificial neural networks (ANNs), trained with cases from process-based modeling. First, we reduce an initial database of ∼1400 observed sandy profiles along the Dutch coastline to 100 representative typological coastal profiles (TCPs). Next, we synthesize a set of plausible extreme storm events, which reproduces the probability distributions and statistical dependencies of offshore wave and water level records. We choose 100 of these events to simulate the dune response of the 100 TCPs using the process-based model XBeach, resulting in 10 000 cases. Using these cases as training data, we design a two-phase meta-model, comprised of a classifying ANN (which predicts the occurrence (or not) of erosion) and a regression ANN (which gives a DEV prediction). Hence, this model may be integrated in early warning systems or allow coastal engineers and managers to upscale storm forcing to dune response investigations to large coastal areas with relative ease.

ATHANASIOU Panagiotis;  VAN DONGEREN Ap;  GIARDINO Alessio;  VOUSDOUKAS Michail;  JOSE Antolinez;  RANASINGHE Roshanka; 

2023-01-16

COPERNICUS GESELLSCHAFT MBH

JRC128324

1561-8633 (online),   

https://nhess.copernicus.org/articles/22/3897/2022/nhess-22-3897-2022.html,    https://publications.jrc.ec.europa.eu/repository/handle/JRC128324,   

10.5194/nhess-22-3897-2022 (online),