TY - JOUR
T1 - Estimating dune erosion at the regional scale using a meta-model based on neural networks
AU - Athanasiou, Panagiotis
AU - Van Dongeren, Ap
AU - Giardino, Alessio
AU - Vousdoukas, Michalis
AU - Antolinez, Jose A.A.
AU - Ranasinghe, Roshanka
N1 - Funding Information:
Ap van Dongeren was funded in part by the Deltares Strategic Research Programme “Natural Hazards” and Alessio Giardino by the Research Programme “Seas and Coastal Zones”. Roshanka Ranasinghe is supported by the AXA research fund and partly supported by the Deltares Research Programme “Seas and Coastal Zones”. The figures were created with the Python 3.7.3 ( https://www.python.org , last access: 2 December 2021) and Matplotlib v3.1.2 ( https://matplotlib.org/ , last access: 2 December 2021) libraries. For the creation and configuration of the artificial neural network, the Keras (Chollet et al., 2015) and TensorFlow (Abadi et al., 2016) libraries were used.
Funding Information:
This work has received funding from the EU Horizon 2020 Program for Research and Innovation (EUCP: “European Climate Prediction system” – grant no. 776613; https://www.eucp-project.eu , last access: 30 November 2022).
Publisher Copyright:
© 2022 Panagiotis Athanasiou et al.
PY - 2022/12/7
Y1 - 2022/12/7
N2 - 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. Process-based numerical models are presently used to assess the morphological changes of dune and beach systems during storms. 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 1/41400 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). Validation against a benchmark dataset created with XBeach and a sparse set of available dune erosion observations shows high prediction skill with a skill score of 0.82. The meta-model can predict post-storm DEV 103-104 times faster (depending on the duration of the storm) than running XBeach. 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.
AB - 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. Process-based numerical models are presently used to assess the morphological changes of dune and beach systems during storms. 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 1/41400 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). Validation against a benchmark dataset created with XBeach and a sparse set of available dune erosion observations shows high prediction skill with a skill score of 0.82. The meta-model can predict post-storm DEV 103-104 times faster (depending on the duration of the storm) than running XBeach. 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.
UR - http://www.scopus.com/inward/record.url?scp=85145591502&partnerID=8YFLogxK
U2 - 10.5194/nhess-22-3897-2022
DO - 10.5194/nhess-22-3897-2022
M3 - Article
AN - SCOPUS:85145591502
SN - 1561-8633
VL - 22
SP - 3897
EP - 3915
JO - Natural hazards and earth system sciences
JF - Natural hazards and earth system sciences
IS - 12
ER -