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| Titel |
Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam |
| VerfasserIn |
A. El-Shafie, A. Noureldin |
| 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 ; 15, no. 3 ; Nr. 15, no. 3 (2011-03-11), S.841-858 |
| Datensatznummer |
250012685
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| Publikation (Nr.) |
 copernicus.org/hess-15-841-2011.pdf |
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| Zusammenfassung |
| Artificial neural networks (ANN) have been found efficient, particularly in
problems where characteristics of the processes are stochastic and difficult
to describe using explicit mathematical models. However, time series
prediction based on ANN algorithms is fundamentally difficult and faces
problems. One of the major shortcomings is the search for the optimal
input pattern in order to enhance the forecasting capabilities for the
output. The second challenge is the over-fitting problem during the training
procedure and this occurs when ANN loses its generalization. In this research,
autocorrelation and cross correlation analyses are suggested as a method for
searching the optimal input pattern. On the other hand, two generalized
methods namely, Regularized Neural Network (RNN) and Ensemble Neural Network
(ENN) models are developed to overcome the drawbacks of classical ANN
models. Using Generalized Neural Network (GNN) helped avoid over-fitting of
training data which was observed as a limitation of classical ANN models.
Real inflow data collected over the last 130 years at Lake Nasser was used
to train, test and validate the proposed model. Results show that the
proposed GNN model outperforms non-generalized neural network and
conventional auto-regressive models and it could provide accurate inflow
forecasting. |
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