Forecasting of the Nile River Inflows by Genetic Algorithms

  • M. E. El-Telbany
  • A. H. Abdel-Wahab
  • S. I. Shaheen
Conference paper


The prediction of time series phenomena is a hard and complex task. The selection of a proper statistical model and the setup of its parameters (in terms of the number of coefficients and their values) is also a difficult task and it is usually solved by trial and error. This paper presents a hybrid system that integrates genetic algorithms and traditional statistical models to overcome the model selection and tuning problem. The system is applied to the domain of river Nile inflows forecasting. This domain is characterized by the availability of large amount of data and prediction models. Finally, the results of applying the proposed system are presented and discussed.


Genetic Algorithm ARMA Model Akaike Information Criterion Traditional Statistical Model River Water Inflow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • M. E. El-Telbany
    • 1
  • A. H. Abdel-Wahab
    • 1
  • S. I. Shaheen
    • 2
  1. 1.Computers and Systems Dept.Electronics Research InstituteDokki, GizaEgypt
  2. 2.Computers Engineering Department, Faculty of EngineeringCairo UniversityEgypt

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