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| Titel |
Comparison of three updating schemes using artificial neural network in flow forecasting |
| VerfasserIn |
Lihua Xiong, Kieran M. O'Connor, Shenglian Guo |
| 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 ; 8, no. 2 ; Nr. 8, no. 2, S.247-255 |
| Datensatznummer |
250005500
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| Publikation (Nr.) |
 copernicus.org/hess-8-247-2004.pdf |
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| Zusammenfassung |
| Three updating schemes using artificial neural network (ANN) in flow forecasting
are compared in terms of model efficiency. The first is the ANN model in the simulation mode
plus an autoregressive (AR) model. For the ANN model in the simulation model, the input
includes the observed rainfall and the previously estimated discharges, while the AR model is
used to forecast the flow simulation errors of the ANN model. The second one is the ANN model
in the updating mode, i.e. the ANN model uses the observed discharge directly together with the
observed rainfall as the input. In this scheme, the weights of the ANN model are obtained by
optimisation and then kept fixed in the procedure of flow forecasting. The third one is also
the ANN model in the updating mode; however, the weights of the ANN model are no longer fixed
but updated at each time step by the backpropagation method using the latest forecast error
of the ANN model. These three updating schemes are tested for flow forecasting on ten
catchments and it is found that the third updating scheme is more effective than the other
two in terms of their efficiency in flow forecasting. Moreover, compared to the first updating
scheme, the third scheme is more parsimonious in terms of the number of parameters, since the
latter does not need any additional correction model. In conclusion, this paper recommends the
ANN model with the backpropagation method, which updates the weights of ANN at each time step
according to the latest forecast error, for use in real-time flow forecasting.
Keywords: artificial neural network (ANN), updating, flow forecasting, backpropagation
method |
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