Optimising training data for ANNs with Genetic Algorithms

Artificial Neural Networks have proven to be good modelling tools in hydrology for rainfall-runoff modelling and hydraulic flow modelling. Representative data sets 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 are used to optimise training data sets. The approach is tested with an existing hydrological model in The Netherlands. The optimised training set resulted in significant better training data.