Articles | Volume 22, issue 12
https://doi.org/10.5194/nhess-22-3897-2022
https://doi.org/10.5194/nhess-22-3897-2022
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

Viewed

Total article views: 2,440 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,799 588 53 2,440 65 50 38
  • HTML: 1,799
  • PDF: 588
  • XML: 53
  • Total: 2,440
  • Supplement: 65
  • BibTeX: 50
  • EndNote: 38
Views and downloads (calculated since 29 Mar 2022)
Cumulative views and downloads (calculated since 29 Mar 2022)

Viewed (geographical distribution)

Total article views: 2,440 (including HTML, PDF, and XML) Thereof 2,321 with geography defined and 119 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Jun 2024
Download
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.
Altmetrics
Final-revised paper
Preprint