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Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 24 | Page No.: 4487-4499
DOI: 10.3923/jas.2008.4487.4499
 
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Neural Network Model for Nile River Inflow Forecasting Based on Correlation Analysis of Historical Inflow Data
A. El-Shafie, A.E. Noureldin, M.R. Taha and H. Basri

Abstract:
Developing river inflow forecast is an essential requirement for reservoir operation. Accurate forecasting results in better control of water availability, more refined operation of reservoirs and improved hydropower generation. Artificial Neural Networks (ANN) models have been determined useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using mathematical models. The ANN forecasting model is established considering the utilization of the inflow pattern of the previous three months. In this study, real inflow data collected over the last 130 years at Lake Nasser upstream Aswan High Dam (AHD) on Nile River, Egypt was used to develop and examine the performance of the proposed method. The results showed that the proposed ANN model was capable of providing monthly inflow forecasting with Relative Error (RE) less than 20%, which is considerably more accurate if compared with the pre-developed regression model. The main merit of this model is to provide accurate source of information for inflow forecasting for better reservoir operation and appropriate long-term water resources management and planning.
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How to cite this article:

A. El-Shafie, A.E. Noureldin, M.R. Taha and H. Basri, 2008. Neural Network Model for Nile River Inflow Forecasting Based on Correlation Analysis of Historical Inflow Data. Journal of Applied Sciences, 8: 4487-4499.

DOI: 10.3923/jas.2008.4487.4499

URL: https://scialert.net/abstract/?doi=jas.2008.4487.4499

 
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