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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 11
Hydrol. Earth Syst. Sci., 15, 3327–3341, 2011
https://doi.org/10.5194/hess-15-3327-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 15, 3327–3341, 2011
https://doi.org/10.5194/hess-15-3327-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Nov 2011

Research article | 04 Nov 2011

Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space

D. Brochero et al.

Related subject area

Subject: Global hydrology | Techniques and Approaches: Mathematical applications
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Cited articles

Alpaydin, E.: Introduction to Machine Learning. Adaptive Computation and Machine Learning, 2nd Edn., The MIT Press, Cambridge, 2010.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Bishop, C. M.: Pattern Recognition and Machine Learning (Information Science and Statistics), ISBN0387310738, Springer-Verlag New York, Inc., Secaucus, NJ, USA,2006.
Boucher, M.-A., Perreault, L., and Anctil, F.: Tools for the assessment of hydrological ensemble forecasts obtained by neural networks, J. Hydroinform., 11, 297–307, https://doi.org/10.2166/hydro.2009.037, 2009.
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