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Successive-station monthly streamflow prediction using different artificial neural network algorithms
Danandeh Mehr, A.; Kahya, E.; Şahin, A. & Nazemosadat, M. J.
Abstract
In this study, applicability of successive-station
prediction models, as a practical alternative to streamflow
prediction in poor rain gauge catchments, has been investigated
using monthly streamflow records of two successive
stations on Çoruh River, Turkey. For this goal, at the first
stage, based on eight different successive-station prediction
scenarios, feed-forward back-propagation (FFBP) neural
network algorithm has been applied as a brute search tool
to find out the best scenario for the river. Then, two other
artificial neural network (ANN) techniques, namely generalized
regression neural network (GRNN) and radial
basis function (RBF) algorithms, were used to generate two
new ANN models for the selected scenario. Ultimately, a
comparative performance study between the different
algorithms has been performed using Nash–Sutcliffe efficiency,
squared correlation coefficient, and root-meansquare
error measures. The results indicated a promising
role of successive-station methodology in monthly
streamflow prediction. Performance analysis showed that
only 1-month-lagged record of both stations was satisfactory
to achieve accurate models with high-efficiency value.
It is also found that the RBF network resulted in higher
performance than FFBP and GRNN in our study domain.
Keywords
Artificial neural networks; Streamflow prediction; Successive stations; Ungauged catchments
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