Articles | Volume 15, issue 11
Hydrol. Earth Syst. Sci., 15, 3327–3341, 2011
Hydrol. Earth Syst. Sci., 15, 3327–3341, 2011

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,, 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,, 2009.