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| Titel |
Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation |
| VerfasserIn |
N. J. Vos, T. H. M. Rientjes |
| 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 ; 9, no. 1/2 ; Nr. 9, no. 1/2 (2005-07-05), S.111-126 |
| Datensatznummer |
250006593
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| Publikation (Nr.) |
 copernicus.org/hess-9-111-2005.pdf |
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| Zusammenfassung |
| The application of Artificial Neural Networks (ANNs) in rainfall-runoff
modelling needs to be researched more extensively in order to appreciate and
fulfil the potential of this modelling approach. This paper reports on the
application of multi-layer feedforward ANNs for rainfall-runoff modelling
of the Geer catchment (Belgium) using both daily and hourly data. The daily
forecast results indicate that ANNs can be considered good alternatives for
traditional rainfall-runoff modelling approaches, but the simulations based
on hourly data reveal timing errors as a result of a dominating
autoregressive component. This component is introduced in model simulations
by using previously observed runoff values as ANN model input, which is a
popular method for indirectly representing the hydrological state of a
catchment. Two possible solutions to this problem of lagged predictions are
presented. Firstly, several alternatives for representation of the
hydrological state are tested as ANN inputs: moving averages over time of
observed discharges and rainfall, and the output of the simple GR4J model
component for soil moisture. A combination of these hydrological state
representers produces good results in terms of timing, but the overall
goodness of fit is not as good as the simulations with previous runoff data.
Secondly, the possibility of using multiple measures of model performance
during ANN training is mentioned. |
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