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| Titel |
Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting |
| VerfasserIn |
A. Y. Shamseldin, A. E. Nasr, K. M. O'Connor |
| 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.671-684 |
| Datensatznummer |
250003665
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| Publikation (Nr.) |
 copernicus.org/hess-6-671-2002.pdf |
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| Zusammenfassung |
| The Multi-Layer
Feed-Forward Neural Network (MLFFNN) is applied in the context of river flow
forecast combination, where a number of rainfall-runoff models are used
simultaneously to produce an overall combined river flow forecast. The operation
of the MLFFNN depends not only on its neuron configuration but also on the
choice of neuron transfer function adopted, which is non-linear for the hidden
and output layers. These models, each having a different structure to simulate
the perceived mechanisms of the runoff process, utilise the information carrying
capacity of the model calibration data in different ways. Hence, in a discharge
forecast combination procedure, the discharge forecasts of each model provide a
source of information different from that of the other models used in the
combination. In the present work, the significance of the choice of the transfer
function type in the overall performance of the MLFFNN, when used in the river
flow forecast combination context, is investigated critically. Five neuron
transfer functions are used in this investigation, namely, the logistic
function, the bipolar function, the hyperbolic tangent function, the arctan
function and the scaled arctan function. The results indicate that the logistic
function yields the best model forecast combination performance.
Keywords: River flow forecast combination, multi-layer feed-forward neural network,
neuron transfer functions, rainfall-runoff models |
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