An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model

  • G. J. Bowden
  • G. C. Dandy
  • H. R. Maier


Artificial Neural Network (ANN) models are highly flexible function approximators, which have shown their utility in a broad range of ecological modelling applications. The rapid emergence of ANN applications in the field of ecological modelling can be attributed to their advantages over standard statistical approaches. Such flexibility provides a powerful tool for forecasting and prediction, however, the large number of parameters that must be selected only serves to complicate the design process. In most practical circumstances, the design of an ANN is heavily based on heuristic trial-and-error processes with only broad rules of thumb to guide along the way.


Root Mean Square Error Input Selection Kohonen Layer Artificial Neural Network Application ASCE Task Committee 
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© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • G. J. Bowden
  • G. C. Dandy
  • H. R. Maier

There are no affiliations available

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