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| Titel |
Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting |
| VerfasserIn |
E. Toth |
| 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 (2009-09-03), S.1555-1566 |
| Datensatznummer |
250011988
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| Publikation (Nr.) |
 copernicus.org/hess-13-1555-2009.pdf |
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| Zusammenfassung |
| This paper presents the application of a modular approach for real-time
streamflow forecasting that uses different system-theoretic rainfall-runoff
models according to the situation characterising the forecast instant. For
each forecast instant, a specific model is applied, parameterised on the
basis of the data of the similar hydrological and meteorological conditions
observed in the past. In particular, the hydro-meteorological conditions are
here classified with a clustering technique based on Self-Organising Maps
(SOM) and, in correspondence of each specific case, different feed-forward
artificial neural networks issue the streamflow forecasts one to six hours
ahead, for a mid-sized case study watershed. The SOM method allows a
consistent identification of the different parts of the hydrograph,
representing current and near-future hydrological conditions, on the basis
of the most relevant information available in the forecast instant, that is,
the last values of streamflow and areal-averaged rainfall. The results show
that an adequate distinction of the hydro-meteorological conditions
characterising the basin, hence including additional knowledge on the
forthcoming dominant hydrological processes, may considerably improve the
rainfall-runoff modelling performance. |
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