![](bg6.png)
Figure 8 shows the prediction error curve where
it can be seen that the average error is not greater
than 0.2 %.
Figure 8: Percentage Error Curve
5 CONCLUSIONS
The results achieved by the use of the proposed
Elman ANNs for river flow prediction indicate that
they are quite adequate for the flow estimation task.
In the investigated application, the average
prediction error of about 0.2% is much less than that
obtained by traditional ANNs using data Windows
(Haykin, 1999) typically in the order of 5%.
Statistical methods used for flow prediction such as
Box & Jenkins and its variations (Box and Jenkins,
1976) yield an average error larger than 10 %.
For future work, suggestions include the use of
fully recurrent ANNs and ocean temperature data
added to the neural network input. Ocean
temperature is known to have a significant influence
on the river flow values so that sort of information
will be certainly useful for the neural network in
consideration.
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