Sheikh Khozani, Z., Bonakdari, H., Zaji, A. (2018). Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming. Scientia Iranica, 25(1), 152-161. doi: 10.24200/sci.2017.4256
Zohreh Sheikh Khozani; Hossein Bonakdari; Amir Hossein Zaji. "Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming". Scientia Iranica, 25, 1, 2018, 152-161. doi: 10.24200/sci.2017.4256
Sheikh Khozani, Z., Bonakdari, H., Zaji, A. (2018). 'Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming', Scientia Iranica, 25(1), pp. 152-161. doi: 10.24200/sci.2017.4256
Sheikh Khozani, Z., Bonakdari, H., Zaji, A. Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming. Scientia Iranica, 2018; 25(1): 152-161. doi: 10.24200/sci.2017.4256
Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming
Department of Civil Engineering, Razi University, Kermanshah, Iran
Abstract
The determination of erosion and deposition patterns in channels requires detailed knowledge and estimations of the bed shear stress. In this investigation, the application of a Genetic Algorithm based Artificial (GAA) neural networkand genetic programming (GP) for predicting bed shear stress in a rectangular channel with rough boundaries. Several input combinations, fitness functions and transfer functions were investigated to determine the best GAA model. Also the effect of various GP operators on estimating bed shear stress was studied. The comparison between the GAA and GP technique abilities in predicting bed shear stress were investigated. The results revealed that the GAA model performs better in predicting the bed shear stress (RMSE = 0.0774), as compared to the GP model (RMSE = 0.0835).
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