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

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.

Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming

“…Many direct and indirect methods have been used to calculate the shear stress along a wetted Some researchers utilized GEP model to solve different hydraulic problems [21][22][23]. Azamathulla and Zahiri [24] used linear genetic programming to predict the ow discharge in a compound open channel.…”