A practical approach to formulate stage–discharge relationship in natural rivers
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.
KeywordsStage discharge Neural networks Rating curve Modeling
The data used in this study were downloaded from the web server of the USGS. The authors wish to thank the staff of the USGS who are associated with data observation, processing, and management of USGS Web sites.
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