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| Titel |
Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin |
| VerfasserIn |
G. A. Corzo, D. P. Solomatine, Hidayat, M. Wit, M. Werner, S. Uhlenbrook, R. K. Price |
| 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 ; 13, no. 9 ; Nr. 13, no. 9 (2009-09-11), S.1619-1634 |
| Datensatznummer |
250011993
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| Publikation (Nr.) |
 copernicus.org/hess-13-1619-2009.pdf |
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| Zusammenfassung |
| One of the challenges in river flow simulation modelling is increasing the
accuracy of forecasts. This paper explores the complementary use of
data-driven models, e.g. artificial neural networks (ANN) to improve the
flow simulation accuracy of a semi-distributed process-based model. The
IHMS-HBV model of the Meuse river basin is used in this research. Two schemes
are tested. The first one explores the replacement of sub-basin models by
data-driven models. The second scheme is based on the replacement of the
Muskingum-Cunge routing model, which integrates the multiple sub-basin
models, by an ANN. The results show that: (1) after a step-wise spatial
replacement of sub-basin conceptual models by ANNs it is possible to increase
the accuracy of the overall basin model; (2) there are time periods when low
and high flow conditions are better represented by ANNs; and (3) the
improvement in terms of RMSE obtained by using ANN for routing is greater than that
when using sub-basin replacements. It can be concluded that the presented two
schemes can improve the performance of process-based models in the context of
flow forecasting. |
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