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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 3
Hydrol. Earth Syst. Sci., 15, 841–858, 2011
https://doi.org/10.5194/hess-15-841-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 15, 841–858, 2011
https://doi.org/10.5194/hess-15-841-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Mar 2011

Research article | 11 Mar 2011

Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam

A. El-Shafie1 and A. Noureldin2 A. El-Shafie and A. Noureldin
  • 1Senior Lecturer, Civil and Structural Engineering Dept. University Kebangsaan Malaysia, Malaysia
  • 2Associate Professor, Electrical and Computer Engineering, Royal Military College, Kingston, Canada

Abstract. 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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