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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3-4/2013

01.09.2013 | Original Article

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

verfasst von: Aytac Guven, Ali Aytek, H. Md. Azamathulla

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

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 (R 2) 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.
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Metadaten
Titel
A practical approach to formulate stage–discharge relationship in natural rivers
verfasst von
Aytac Guven
Ali Aytek
H. Md. Azamathulla
Publikationsdatum
01.09.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1011-5

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