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| Titel |
Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling |
| VerfasserIn |
N. J. Vos |
| 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 ; 17, no. 1 ; Nr. 17, no. 1 (2013-01-22), S.253-267 |
| Datensatznummer |
250017688
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| Publikation (Nr.) |
 copernicus.org/hess-17-253-2013.pdf |
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| Zusammenfassung |
| Despite theoretical benefits of recurrent artificial neural networks over their feedforward
counterparts, it is still unclear whether the former offer practical advantages as
rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of
the training procedure due to their architecture. This work uses the recently introduced and conceptually
simple echo state networks for streamflow forecasts on twelve river basins in the
Eastern United States, and compares them to a variety of traditional feedforward and recurrent
approaches. Two modifications on the echo state network models are made that increase the
hydrologically relevant information content of their internal state. The results show that the
echo state networks outperform feedforward networks and are competitive with
state-of-the-art recurrent networks, across a range of performance measures. This, along with
their simplicity and ease of training, suggests that they can be considered
promising alternatives to traditional artificial neural networks in rainfall–runoff modelling. |
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