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Using two soft computing methods to predict wall and bed shear stress in smooth rectangular channels

Author:
Khozani, Zohreh Sheikh, Bonakdari, Hossein, Zaji, Amir Hossein
Source:
Appl Water Sci 2017 v.7 no.7 pp. 3973-3983
ISSN:
2190-5487
Subject:
algorithms, data collection, equations, neural networks, prediction, shear stress
Abstract:
Two soft computing methods were extended in order to predict the mean wall and bed shear stress in open channels. The genetic programming (GP) and Genetic Algorithm Artificial Neural Network (GAA) were investigated to determine the accuracy of these models in estimating wall and bed shear stress. The GP and GAA model results were compared in terms of testing dataset in order to find the best model. In modeling both bed and wall shear stress, the GP model performed better with RMSE of 0.0264 and 0.0185, respectively. Then both proposed models were compared with equations for rectangular open channels, trapezoidal channels and ducts. According to the results, the proposed models performed the best in predicting wall and bed shear stress in smooth rectangular channels. The obtained equation for rectangular channels could estimate values closer to experimental data, but the equations for ducts had poor, inaccurate results in predicting wall and bed shear stress. The equation presented for trapezoidal channels did not have acceptable accuracy in predicting wall and bed shear stress either.
Agid:
5835653