Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/hess-16-3049-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-16-3049-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 16, issue 8
Research article
 | 
29 Aug 2012
Research article |  | 29 Aug 2012

Ideal point error for model assessment in data-driven river flow forecasting

C. W. Dawson, N. J. Mount, R. J. Abrahart, and A. Y. Shamseldin

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Abrahart, R. J. and See, L. M.: Neural network modelling of non-linear hydrological relationships, Hydrol. Earth Syst. Sci., 11, 1563–1579, https://doi.org/10.5194/hess-11-1563-2007, 2007.
Abrahart, R. J., Mount, N. J., Ab Ghani, N., Clifford, N. J., and Dawson, C. W.: DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling, J. Hydrol., 409, 596–611, 2011.
Abrahart, R. J., Antcil, 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, Progr. Phys. Geogr., 36, 480–513, 2012a.
Abrahart, R. J., Dawson, C. W., and Mount, N. J.: Partial derivative sensitivity analysis applied to neural network forecasting, Proceedings 10th International Conference on Hydroinformatics, 14–18 July 2012, Hamburg, Germany, 2012b.
American Society of Civil Engineers: Criteria for evaluation of watershed models, J. Irrig. Drain. Eng.-ASCE, 119, 429–442, 1993.
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