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

• Published in: International journal of environmental science and technology (Tehran) Vol. 12; no. 7; pp. 2191 - 2200
• Main Authors: Danandeh Mehr, A, Kahya, E, Şahin, A, Nazemosadat, M. J
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Center for Environment and Energy Research and Studies (CEERS) 01.07.2015; Springer Berlin Heidelberg
• Subjects: algorithms; Aquatic Pollution; Artificial neural networks; correlation; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Science and Engineering; neural networks; Original Paper; prediction; rain gauges; rivers; Soil Science & Conservation; stream flow; Streamflow prediction; Successive stations; Turkey (country); Ungauged catchments; Waste Water Technology; Water Management; Water Pollution Control; watersheds; Turkey (country); Streamflow prediction; Ungauged catchments; Successive stations; Artificial neural networks
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/s13762-014-0613-0

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.