Abstract
Neural networks are now extensively used for rainfall–runoff modelling. Considered a ”black box” approach, they allow non-linear relationships to be modelled without explicit knowledge of the underlying physical processes. This method offers many advantages over earlier equation-based models. However, neural networks have their own set of issues that need to be resolved, the most important of which is how to best train the network. Genetic algorithms (GAs) represent one method for training or breeding a neural network. This study uses JavaSANE, a system that advances on traditional evolutionary approaches by evolving and optimising individual neurons. This method is used to evolve good performing neural network rainfall–runoff solutions for the River Ouse catchment in the UK. The results show that as lead times increase, the JavaSANE networks outperform conventional feedforward networks trained with backpropagation.
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Heppenstall, A., See, L., Abrahart, R., Dawson, C. (2009). Neural Network Hydrological Modelling: An Evolutionary Approach. In: Abrahart, R.J., See, L.M., Solomatine, D.P. (eds) Practical Hydroinformatics. Water Science and Technology Library, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79881-1_23
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DOI: https://doi.org/10.1007/978-3-540-79881-1_23
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