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Optimising training data for ANNs with Genetic Algorithms
1Section of Water Resources, Delft University of Technology, Delft, The Netherlands
2MX.Systems B.V., Rijswijk, The Netherlands
Abstract. Artificial Neural Networks (ANNs) have proved to be good modelling tools in hydrology for rainfall-runoff modelling and hydraulic flow modelling. Representative datasets are necessary for the training phase in which the ANN learns the model's input-output relations. Good and representative training data is not always available. In this publication Genetic Algorithms (GA) are used to optimise training datasets. The approach is tested with an existing hydraulic model in The Netherlands. An initial trainnig dataset is used for training the ANN. After optimisation with a GA of the training dataset the ANN produced more accurate model results.
Final Revised Paper (PDF, 546 KB) Discussion Paper (HESSD)
Citation: Kamp, R. G. and Savenije, H. H. G.: Optimising training data for ANNs with Genetic Algorithms, Hydrol. Earth Syst. Sci., 10, 603-608, 2006. Bibtex EndNote Reference Manager
