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| Titel |
Optimising training data for ANNs with Genetic Algorithms |
| VerfasserIn |
R. G. Kamp, H. H. G. Savenije |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 10, no. 4 ; Nr. 10, no. 4 (2006-09-07), S.603-608 |
| Datensatznummer |
250008145
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| Publikation (Nr.) |
 copernicus.org/hess-10-603-2006.pdf |
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| Zusammenfassung |
| 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. |
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