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| Titel |
Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts |
| VerfasserIn |
N. Lauzon, F. Anctil, C. W. Baxter |
| 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 ; 10, no. 4 ; Nr. 10, no. 4 (2006-07-07), S.485-494 |
| Datensatznummer |
250008136
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| Publikation (Nr.) |
 copernicus.org/hess-10-485-2006.pdf |
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| Zusammenfassung |
| This work addresses the issue of better considering the heterogeneity of
precipitation fields within lumped rainfall-runoff models where only areal
mean precipitation is usually used as an input. A method using a Kohonen
neural network is proposed for the clustering of precipitation fields.
The evaluation and improvement of the performance of a lumped
rainfall-runoff model for one-day ahead predictions is then established
based on this clustering. Multilayer perceptron neural networks are
employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in
France, which is equipped with 23 rain gauges with data for a 21-year
period, is employed as the application case. The results demonstrate the
relevance of the proposed clustering method, which produces groups of
precipitation fields that are in agreement with the global climatological
features affecting the region, as well as with the topographic constraints
of the watershed (i.e., orography). The strengths and weaknesses of the
rainfall-runoff models are highlighted by the analysis of their performance
vis-à-vis the clustering of precipitation fields. The results also
show the capability of multilayer perceptron neural networks to account for
the heterogeneity of precipitation, even when built as lumped
rainfall-runoff models. |
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