Articles | Volume 22, issue 12
Research article
07 Dec 2022
Research article |  | 07 Dec 2022

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

Panagiotis Athanasiou, Ap van Dongeren, Alessio Giardino, Michalis Vousdoukas, Jose A. A. Antolinez, and Roshanka Ranasinghe

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Revised manuscript accepted for ESSD
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Cited articles

Almar, R., Ranasinghe, R., Bergsma, E. W. J., Diaz, H., Melet, A., Papa, F., Vousdoukas, M., Athanasiou, P., Dada, O., Almeida, L. P., and Kestenare, E.: A global analysis of extreme coastal water levels with implications for potential coastal overtopping, Nat. Commun., 12, 3775,, 2021. 
Antolínez, J. A. A., Méndez, F. J., Anderson, D., Ruggiero, P., and Kaminsky, G. M.: Predicting Climate-Driven Coastlines With a Simple and Efficient Multiscale Model, J. Geophys. Res.-Earth Surf., 124, 1596–1624,, 2019. 
Arcadis/Deltares: Validation of dune erosion model XBeach. Development of “BOI Sandy Coasts,” Tech. report D10029117:2.0., 2022. 
Athanasiou, P., de Boer, W., Yoo, J., Ranasinghe, R., and Reniers, A.: Analysing decadal-scale crescentic bar dynamics using satellite imagery: A case study at Anmok beach, South Korea, Mar. Geol., 405, 1–11,, 2018. 
Short summary
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.
Final-revised paper