Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods

TOPRAK Z. F., Cığızoğlu H. K.

HYDROLOGICAL PROCESSES, vol.22, no.20, pp.4106-4129, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 20
  • Publication Date: 2008
  • Doi Number: 10.1002/hyp.7012
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.4106-4129
  • Istanbul Technical University Affiliated: Yes


In this study, three artficial 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 streems. 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 precisily than existing equations. This method of predicting the longitudinal dispersion coefficient i river flows was tested on 65 data sets, obtained by researchers from 30 rivers in the USA. Results methods considered. Copyright (C) 2008 John Wiley & Sons. Ltd.