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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 5
Hydrol. Earth Syst. Sci., 11, 1563–1579, 2007
https://doi.org/10.5194/hess-11-1563-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Data-driven approaches, optimization and model integration:...

Hydrol. Earth Syst. Sci., 11, 1563–1579, 2007
https://doi.org/10.5194/hess-11-1563-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  20 Sep 2007

20 Sep 2007

Neural network modelling of non-linear hydrological relationships

R. J. Abrahart1 and L. M. See2 R. J. Abrahart and L. M. See
  • 1School of Geography, University of Nottingham, Nottingham NG7 2RD, UK
  • 2School of Geography, University of Leeds, Leeds, LS2 9JT, UK

Abstract. Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfall-runoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling non-linear hydrological processes and are therefore appropriate tools for hydrological modelling.

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