Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods

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 dispers...

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Bibliographic Details
Published in:Hydrological processes 2008-09, Vol.22 (20), p.4106-4129
Main Authors: Toprak, Z. Fuat, Cigizoglu, Hikmet Kerem
Format: Article
Language:English
Subjects:
ANN
concentration
dispersion
dispersion coefficient
Earth sciences
Earth, ocean, space
Exact sciences and technology
Hydrology
Hydrology. Hydrogeology
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Summary: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.
ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.7012