Articles | Volume 13, issue 9
https://doi.org/10.5194/hess-13-1619-2009
© Author(s) 2009. 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-13-1619-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
11 Sep 2009
Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin
G. A. Corzo
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
D. P. Solomatine
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Water Resources Section, Delft University of Technology, Delft, The Netherlands
Hidayat
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Centre for Limnology, Indonesian Institute of Sciences, Cibinong, Indonesia
M. de Wit
Deltares (Delft|Hydraulics), Rotterdamseweg 185, Delft, The Netherlands
M. Werner
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Deltares (Delft|Hydraulics), Rotterdamseweg 185, Delft, The Netherlands
S. Uhlenbrook
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Vrije Universiteit Amsterdam, Faculteit der Aardwetenschappen, Amsterdam, The Netherlands
R. K. Price
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Water Resources Section, Delft University of Technology, Delft, The Netherlands
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- Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski https://doi.org/10.1016/j.jhydrol.2011.06.019
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- A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network G. Humphrey et al. https://doi.org/10.1016/j.jhydrol.2016.06.026
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- A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. https://doi.org/10.5194/hess-16-4417-2012
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. https://doi.org/10.1080/02626667.2016.1159683
- Wavelet analyses of neural networks based river discharge decomposition L. Campozano et al. https://doi.org/10.1002/hyp.13726
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