@article{(Open Science Index):https://publications.waset.org/pdf/10010768,
	  title     = {Modified Hybrid Genetic Algorithm-Based Artificial Neural Network Application on Wall Shear Stress Prediction},
	  author    = {Zohreh Sheikh Khozani and  Wan Hanna Melini Wan Mohtar and  Mojtaba Porhemmat},
	  country	= {},
	  institution	= {},
	  abstract     = {Prediction of wall shear stress in a rectangular channel, with non-homogeneous roughness distribution, was studied. Estimation of shear stress is an important subject in hydraulic engineering, since it affects the flow structure directly. In this study, the Genetic Algorithm Artificial (GAA) neural network is introduced as a hybrid methodology of the Artificial Neural Network (ANN) and modified Genetic Algorithm (GA) combination. This GAA method was employed to predict the wall shear stress. Various input combinations and transfer functions were considered to find the most appropriate GAA model. The results show that the proposed GAA method could predict the wall shear stress of open channels with high accuracy, by Root Mean Square Error (RMSE) of 0.064 in the test dataset. Thus, using GAA provides an accurate and practical simple-to-use equation.
},
	    journal   = {International Journal of Civil and Environmental Engineering},
	  volume    = {13},
	  number    = {9},
	  year      = {2019},
	  pages     = {605 - 609},
	  ee        = {https://publications.waset.org/pdf/10010768},
	  url   	= {https://publications.waset.org/vol/153},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 153, 2019},
	}