Successive-station monthly streamflow prediction using different artificial neural network algorithms

A. Danandeh Mehr*, E. Kahya, A. Şahin, M. J. Nazemosadat

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

82 Citations (Scopus)

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-mean-square 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.

Original languageEnglish
Pages (from-to)2191-2200
Number of pages10
JournalInternational Journal of Environmental Science and Technology
Volume12
Issue number7
DOIs
Publication statusPublished - 10 Jul 2015

Bibliographical note

Publisher Copyright:
© 2014, Islamic Azad University (IAU).

Keywords

  • Artificial neural networks
  • Streamflow prediction
  • Successive stations
  • Ungauged catchments

Fingerprint

Dive into the research topics of 'Successive-station monthly streamflow prediction using different artificial neural network algorithms'. Together they form a unique fingerprint.

Cite this