2006 Special issue: Symbiotic adaptive neuro-evolution applied to rainfall-runoff modelling in northern England

  • Authors:

  • Christian W. Dawson;Linda M. See;Robert J. Abrahart;Alison J. Heppenstall

  • Affiliations:

  • Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK;School of Geography, University of Leeds, Leeds LS2 9JT, UK;School of Geography, University of Nottingham, Nottingham NG7 2RD, UK;School of Geography, University of Leeds, Leeds LS2 9JT, UK

  • Venue:
  • Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
  • Year:
  • 2006

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Abstract

This paper uses a symbiotic adaptive neuro-evolutionary algorithm to breed neural network models for the River Ouse catchment. It advances on traditional evolutionary approaches by evolving and optimising individual neurons. Furthermore, it is ideal for experimentation with alternative objective functions. Recent research suggests that sum squared error may not result in the most appropriate models from a hydrological perspective. Models are bred for lead times of 6 and 24hours and compared with conventional neural network models trained using backpropagation. The algorithm is also modified to use different objective functions in the optimisation process: mean squared error, relative error and the Nash-Sutcliffe coefficient of efficiency. The results show that at longer lead times the evolved neural networks outperform the conventional ones in terms of overall performance. It is also shown that the sum squared error objective function does not result in the best performing model from a hydrological perspective.