Neural Computing and Applications

, Volume 23, Issue 3–4, pp 873–880

A practical approach to formulate stage–discharge relationship in natural rivers

Original Article

DOI: 10.1007/s00521-012-1011-5

Cite this article as:
Guven, A., Aytek, A. & Azamathulla, H.M. Neural Comput & Applic (2013) 23: 873. doi:10.1007/s00521-012-1011-5


This study proposes a new formulation technique for modeling stage–discharge relationship, as an alternative approach to standard regression techniques. An explicit neural network formulation (ENNF) is derived by using data obtained from United States Geological Survey data base. The neural network model is trained and tested using time series of daily stage and discharge data from two stations in Pennsylvania, USA. The model is compared with the standard rating curve (SRC) technique. Statistical parameters such as average, standard deviation, minimum, and maximum values, as well as criteria such as root mean square error, the efficiency coefficient (E), and determination coefficient (R2) are used to measure the performance of the ENNF. Considerably, well performance is achieved in modeling streamflow by using ENNF. The comparison results reveal that the suggested formulations perform better than the conventional SRC.


Stage discharge Neural networks Rating curve Modeling 

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of GaziantepGaziantepTurkey
  2. 2.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaNibong TebalMalaysia
  3. 3.Sahinbey MunicipalityGaziantepTurkey

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