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Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods
Author(s) -
Toprak Z. Fuat,
Cigizoglu Hikmet Kerem
Publication year - 2008
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.7012
Subject(s) - dispersion (optics) , artificial neural network , streams , function (biology) , computer science , regression , radial basis function , linear regression , statistics , data mining , mathematics , artificial intelligence , physics , optics , computer network , evolutionary biology , biology
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.