Status: this preprint is currently under review for the journal NHESS.
Estimating dune erosion at the regional scale using a meta-model based on Neural Networks
Panagiotis Athanasiou1,2,Ap van Dongeren1,3,Alessio Giardino4,Michalis Vousdoukas5,Jose A. A. Antolinez6,and Roshanka Ranasinghe1,2,3Panagiotis Athanasiou et al.Panagiotis Athanasiou1,2,Ap van Dongeren1,3,Alessio Giardino4,Michalis Vousdoukas5,Jose A. A. Antolinez6,and Roshanka Ranasinghe1,2,3
Received: 24 Mar 2022 – Discussion started: 29 Mar 2022
Abstract. Sandy beaches and dune systems have high recreational and ecological value, while 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 (ANN), trained with cases from process-based modelling. First, we reduce an initial database of ~1,400 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 2-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-dune response investigations to large coastal areas with relative ease.
Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can efficiently predict dune erosion are critical for coastal zone management and early warning systems. Here we develop such a model for the Dutch coast based on machine learning techniques, allowing for dune erosion estimations in a matter of seconds relative to available computationally expensive models. Validation of the model against benchmark data and observations shows good agreement.
Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can...