Titlebook: Artificial Neural Networks in Hydrology; R. S. Govindaraju,A. Ramachandra Rao Book 2000 Springer Science+Business Media B.V. 2000 artifici

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J. H. Werner,R. Bergmann,R. Brendel application of ANNs to long range precipitation prediction in California using large-scale climatological parameters. In addition, a determination will be made about extracting information from the trained network in order to learn how the ANN’s “thinking” produces the prediction.

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https://doi.org/10.1007/978-94-015-9341-0artificial neural network; groundwater; hydrology; learning; linear optimization; modeling; network; neural

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https://doi.org/10.1007/BFb0108003onal means. This chapter addresses some issues related to the training of the class of ANNs known as Multi-layer Feedforward Neural Networks (MFNN) which are most commonly used in streamflow forecasting applications. We also present results illustrating the applicability of properly trained MFNNs in

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