Articles | Volume 19, issue 10
https://doi.org/10.5194/nhess-19-2295-2019
https://doi.org/10.5194/nhess-19-2295-2019
Research article
 | Highlight paper
 | 
22 Oct 2019
Research article | Highlight paper |  | 22 Oct 2019

Ensemble models from machine learning: an example of wave runup and coastal dune erosion

Tomas Beuzen, Evan B. Goldstein, and Kristen D. Splinter

Viewed

Total article views: 4,255 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,037 1,169 49 4,255 59 58
  • HTML: 3,037
  • PDF: 1,169
  • XML: 49
  • Total: 4,255
  • BibTeX: 59
  • EndNote: 58
Views and downloads (calculated since 02 Apr 2019)
Cumulative views and downloads (calculated since 02 Apr 2019)

Viewed (geographical distribution)

Total article views: 4,255 (including HTML, PDF, and XML) Thereof 3,529 with geography defined and 726 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 28 Mar 2024
Download
Short summary
Wave runup is important for characterizing coastal vulnerability to wave action; however, it is complex and uncertain to predict. We use machine learning with a high-resolution dataset of wave runup to develop an accurate runup predictor that includes prediction uncertainty. We show how uncertainty in wave runup predictions can be used practically in a model of dune erosion to make ensemble predictions that provide more information and greater predictive skill than a single deterministic model.
Altmetrics
Final-revised paper
Preprint