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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-106', Víctor Malagón-Santos, 16 May 2022
  • RC2: 'Comment on nhess-2022-106', Anonymous Referee #2, 22 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (09 Oct 2022) by Joanna Staneva
AR by Panagiotis Athanasiou on behalf of the Authors (09 Oct 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (10 Oct 2022) by Joanna Staneva
RR by Víctor Malagón-Santos (13 Oct 2022)
RR by Anonymous Referee #2 (14 Oct 2022)
ED: Publish as is (23 Oct 2022) by Joanna Staneva
ED: Publish as is (24 Oct 2022) by Piero Lionello (Executive editor)
AR by Panagiotis Athanasiou on behalf of the Authors (28 Oct 2022)
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