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| Titel |
Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology? |
| VerfasserIn |
E. Gaume, R. Gosset |
| 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 ; 7, no. 5 ; Nr. 7, no. 5, S.693-706 |
| Datensatznummer |
250004790
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| Publikation (Nr.) |
 copernicus.org/hess-7-693-2003.pdf |
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| Zusammenfassung |
| Recently Feed-Forward Artificial Neural Networks (FNN) have been gaining
popularity for stream flow forecasting. However, despite the promising results presented
in recent papers, their use is questionable. In theory, their “universal approximator‿
property guarantees that, if a sufficient number of neurons is selected, good performance
of the models for interpolation purposes can be achieved. But the choice of a more complex
model does not ensure a better prediction. Models with many parameters have a high capacity
to fit the noise and the particularities of the calibration dataset, at the cost of
diminishing their generalisation capacity. In support of the principle of model parsimony,
a model selection method based on the validation performance of the models, "traditionally"
used in the context of conceptual rainfall-runoff modelling, was adapted to the choice of
a FFN structure. This method was applied to two different case studies: river flow
prediction based on knowledge of upstream flows, and rainfall-runoff modelling. The
predictive powers of the neural networks selected are compared to the results obtained
with a linear model and a conceptual model (GR4j). In both case studies, the method leads
to the selection of neural network structures with a limited number of neurons in the
hidden layer (two or three). Moreover, the validation results of the selected FNN and of
the linear model are very close. The conceptual model, specifically dedicated to
rainfall-runoff modelling, appears to outperform the other two approaches. These
conclusions, drawn on specific case studies using a particular evaluation method, add
to the debate on the usefulness of Artificial Neural Networks in hydrology.
Keywords: forecasting; stream-flow; rainfall-runoff; Artificial Neural Networks |
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