Articles | Volume 21, issue 8
https://doi.org/10.5194/hess-21-4021-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-4021-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Aug 2017
Research article |
| 10 Aug 2017
Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
IHE Delft Institute for Water Education, Delft, the Netherlands
Deltares, Delft, the Netherlands
currently at: Institute of Environmental Engineering, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
currently at: Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland
Joost V. L. Beckers
Deltares, Delft, the Netherlands
Albrecht H. Weerts
Deltares, Delft, the Netherlands
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
Dimitri P. Solomatine
IHE Delft Institute for Water Education, Delft, the Netherlands
Water Resources Section, Delft University of Technology, Delft, the Netherlands
Water Problems Institute of RAS, Moscow, Russia
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Discussed (final revised paper)
Latest update: 20 Nov 2024
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.
We generate uncertainty intervals for hydrologic model predictions using a simple instance-based...
