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| Titel |
Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China |
| VerfasserIn |
C. W. Dawson, C. Harpham, R. L. Wilby, Y. Chen |
| 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 ; 6, no. 4 ; Nr. 6, no. 4, S.619-626 |
| Datensatznummer |
250003661
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| Publikation (Nr.) |
 copernicus.org/hess-6-619-2002.pdf |
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| Zusammenfassung |
| While engineers have been
quantifying rainfall-runoff processes since the mid-19th century, it is only in
the last decade that artificial neural network models have
been applied to the same task. This paper evaluates two neural networks in this
context: the popular multilayer perceptron (MLP), and the
radial basis function network (RBF). Using six-hourly rainfall-runoff data for
the River Yangtze at Yichang (upstream of the Three Gorges
Dam) for the period 1991 to 1993, it is shown that both neural network types can
simulate river flows beyond the range of the training set.
In addition, an evaluation of alternative RBF transfer functions demonstrates
that the popular Gaussian function, often used in RBF
networks, is not necessarily the ‘best’ function to use for river flow
forecasting. Comparisons are also made between these neural networks and
conventional statistical techniques; stepwise multiple linear regression, auto
regressive moving average models and a zero order forecasting approach.
Keywords: Artificial neural network, multilayer perception, radial basis
function, flood forecasting |
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