Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-129-2022
https://doi.org/10.5194/hess-26-129-2022
Research article
 | 
12 Jan 2022
Research article |  | 12 Jan 2022

Parsimonious statistical learning models for low-flow estimation

Johannes Laimighofer, Michael Melcher, and Gregor Laaha

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Cited articles

Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geog., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012. a
Ambroise, C. and McLachlan, G. J.: Selection bias in gene extraction on the basis of microarray gene-expression data, P. Natl. Acad. Sci. USA, 99, 6562–6566, https://doi.org/10.1073/pnas.102102699, 2002. a, b
Beguería, S. and Vicente-Serrano, S. M.: SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index, r package version 1.7, available at: https://CRAN.R-project.org/package=SPEI (last access: 15 Septepmber 2021), 2017. a
Blöschl, G., Sivapalan, M., Wagener, T., Savenije, H., and Viglione, A.: Runoff prediction in ungauged basins: synthesis across processes, places and scales, edited by: Blöschl, G., Wagener, T., and Savenije, H. Cambridge University Press, https://doi.org/10.1017/CBO9781139235761, 2013. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
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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.
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