Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9743
Title: Fuzzy, ANN, and regression models to predict longitudinal dispersion coefficient in natural streams
Authors: Tayfur, Gökmen
Keywords: aNN
calibration
dispersion coefficient
fuzzy
modeling
regression
validation
Issue Date: 2006
Publisher: IWA Publishing
Abstract: This study developed fuzzy, ANN, and regression-based models to predict longitudinal dispersion coefficient in natural streams from flow discharge data. 92 sets of field data were employed to calibrate and validate the models. 63 sets of data were used for the calibration while the remaining data were used for the validation of the models. The model-prediction results revealed the superiority of the developed models over the existing equations. The developed models predicted the measured data satisfactorily with minimum errors and maximum accuracy rates. The three models had comparable performances although the fuzzy model had the highest accuracy rate (79%) and lowest mean relative error (0.85).
URI: https://doi.org/10.2166/nh.2006.005
https://hdl.handle.net/11147/9743
ISSN: 2224-7955
0029-177
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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