Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jan 2022
Research article |
| 12 Jan 2022
| 12 Jan 2022
Parsimonious statistical learning models for low-flow estimation
Johannes Laimighofer
CORRESPONDING AUTHOR
Institute of Statistics, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Michael Melcher
Institute of Information Management, FH JOANNEUM – University of Applied Sciences, Graz, Austria
Gregor Laaha
Institute of Statistics, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
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Short summary
This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.
This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in...
