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2003 | OriginalPaper | Buchkapitel

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

verfasst von : G. J. Bowden, G. C. Dandy, H. R. Maier

Erschienen in: Ecological Informatics

Verlag: Springer Berlin Heidelberg

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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.

Metadaten
Titel
An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model
verfasst von
G. J. Bowden
G. C. Dandy
H. R. Maier
Copyright-Jahr
2003
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-05150-4_11