Articles | Volume 3, issue 4
https://doi.org/10.5194/hess-3-529-1999
© Author(s) 1999. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
https://doi.org/10.5194/hess-3-529-1999
© Author(s) 1999. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
31 Dec 1999
A comparison of artificial neural networks used for river forecasting
C. W. Dawson
Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, UK
School of Environmental and Applied Science, University of Derby, Kedleston Road, Derby, DE22 1GB, UK
email address for corresponding author: C.W.Dawson1@lboro.ac.uk
R. L. Wilby
National Center for Atmospheric Research, Boulder, Colorado 80307-3000, USA
School of Environmental and Applied Science, University of Derby, Kedleston Road, Derby, DE22 1GB, UK
email address for corresponding author: C.W.Dawson1@lboro.ac.uk
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- Aceleración de simulaciones fluviales computacionalmente intensivas mediante aprendizaje automático K. Alvarez Segales et al. https://doi.org/10.22201/dgtic.26832968e.2026.15.157
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- Advances in coupling machine learning with hydrological simulation: A review Y. Yan et al. https://doi.org/10.1016/j.wse.2026.01.002
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- Rainfall-runoff model usingan artificial neural network approach S. Riad et al. https://doi.org/10.1016/j.mcm.2004.10.012
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- Strategic Revenue Management under Uncertainty: A Case Study on Real Estate Projects in India S. Padhi et al. https://doi.org/10.1002/mcda.1537
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- Towards Safer Data-Driven Forecasting of Extreme Streamflows J. Matos et al. https://doi.org/10.1007/s11269-017-1834-z
- A comprehensive survey on conventional and modern neural networks: application to river flow forecasting M. Zounemat-Kermani et al. https://doi.org/10.1007/s12145-021-00599-1
- Methods to improve neural network performance in daily flows prediction C. Wu et al. https://doi.org/10.1016/j.jhydrol.2009.03.038
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- Future climate scenarios and rainfall–runoff modelling in the Upper Gallego catchment (Spain) C. Bürger et al. https://doi.org/10.1016/j.envpol.2007.02.002
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