Articles | Volume 21, issue 8
https://doi.org/10.5194/hess-21-4021-2017
https://doi.org/10.5194/hess-21-4021-2017
Research article
 | 
10 Aug 2017
Research article |  | 10 Aug 2017

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

Omar Wani, Joost V. L. Beckers, Albrecht H. Weerts, and Dimitri P. Solomatine

Viewed

Total article views: 3,464 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,353 1,016 95 3,464 62 86
  • HTML: 2,353
  • PDF: 1,016
  • XML: 95
  • Total: 3,464
  • BibTeX: 62
  • EndNote: 86
Views and downloads (calculated since 16 Mar 2017)
Cumulative views and downloads (calculated since 16 Mar 2017)

Viewed (geographical distribution)

Total article views: 3,464 (including HTML, PDF, and XML) Thereof 3,328 with geography defined and 136 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 28 Mar 2024
Download
Short summary
We generate uncertainty intervals for hydrologic model predictions using a simple instance-based learning scheme. Errors made by the model in some specific hydrometeorological conditions in the past are used to predict the probability distribution of its errors during forecasting. We test it for two different case studies in England. We find that this technique, even though conceptually simple and easy to implement, performs as well as some other sophisticated uncertainty estimation methods.