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Summary: 402 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 2, MARCH 1999
A Comparison Between Neural-Network Forecasting
Techniques--Case Study: River Flow Forecasting
Amir F. Atiya, Senior Member, IEEE, Suzan M. El-Shoura,
Samir I. Shaheen, Member, IEEE, and Mohamed S. El-Sherif
Abstract--Estimating the flows of rivers can have significant
economic impact, as this can help in agricultural water man-
agement and in protection from water shortages and possible
flood damage. The first goal of this paper is to apply neural
networks to the problem of forecasting the flow of the River Nile
in Egypt. The second goal of the paper is to utilize the time series
as a benchmark to compare between several neural-network
forecasting methods. We compare between four different methods
to preprocess the inputs and outputs, including a novel method
proposed here based on the discrete Fourier series. We also
compare between three different methods for the multistep ahead
forecast problem: the direct method, the recursive method, and
the recursive method trained using a backpropagation through
time scheme. We also include a theoretical comparison between
these three methods. The final comparison is between different
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