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

Related subject area

Subject: Engineering Hydrology | Techniques and Approaches: Theory development
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Cited articles

Abrahart, R. J. and See, L. M.: Neural network modelling of non-linear hydrological relationships, Hydrol. Earth Syst. Sci., 11, 1563–1579,, 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.