Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods |
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| Authors: | Z Fuat Toprak Hikmet Kerem Cigizoglu |
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| Institution: | 1. Dicle University, Engineering and Architecture Faculty, Civil Engineering Department, 21280 Diyarbakir, Turkey;2. Istanbul Technical University, Faculty of Civil Engineering, Hydraulics Division, 34469 Istanbul, Turkey |
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| Abstract: | In this study, three artificial neural network methods, i.e. feed forward back propagation, the radial basis function neural network, and the generalized regression neural network are employed to compute the longitudinal dispersion coefficient in order to evaluate its behaviour in predicting dispersion characteristics in natural streams. These methods, which use hydraulic and geometrical data to predict dispersion coefficients, can easily be applied to natural streams and are proven to be superior in explaining their dispersion characteristics more precisely than existing equations. This method of predicting the longitudinal dispersion coefficient in river flows was tested on 65 data sets, obtained by researchers from 30 rivers in the USA. Results using the models are compared with results obtained in many other studies, and are shown to be more accurate than the other methods considered. Copyright © 2008 John Wiley & Sons, Ltd. |
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| Keywords: | ANN dispersion dispersion coefficient concentration |
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