Articles | Volume 17, issue 7
Hydrol. Earth Syst. Sci., 17, 2827–2843, 2013
https://doi.org/10.5194/hess-17-2827-2013
Hydrol. Earth Syst. Sci., 17, 2827–2843, 2013
https://doi.org/10.5194/hess-17-2827-2013

Research article 17 Jul 2013

Research article | 17 Jul 2013

Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework

N. J. Mount et al.

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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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, https://doi.org/10.5194/hess-11-1563-2007, 2007.
Abrahart, R. J., See, L. M., and Kneale, P. E.: Using pruning algorithms and genetic algorithms to optimise neural network architectures and forecasting inputs in a neural network rainfall-runoff model, J. Hydroinform., 1, 103–114, 1999.
Abrahart, R. J., See, L. M., and Kneale, P. E.: Investigating the role of saliency analysis with a neural network rainfall-runoff model, Comput. Geosci., 27, 921–928, 2001.
Abrahart, R. J., Ab Ghani, N., and Swan, J.: Discussion of "An explicit neural network formulation for evapotranspiration", Hydrolog. Sci. J., 54, 382–388, 2009.
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.
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