Gate in general, a device in which a leaf or a member is moved across the water from external position to control or stop the flow. Under flow gates commonly used to regulate and measure flow in hydraulic structures. In this paper, Multiplayer feed forward Artificial Network (ANN) with back propagation algorithm is used to develop a computational model to predict discharge below gates. A network of size 3-9-1 is found suitable for this purpose with 540 iterations and hyperbolic tangent (tanch) activation function. The results of the trained, verified and tested ANN model are compared to the experimental measurements. The results indicated that the ANNs are powerful tools for modeling flow rates below gates.
K.A., A. (2008). ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES. JES. Journal of Engineering Sciences, 36(No 3), 581-587. doi: 10.21608/jesaun.2008.116136
MLA
Amen, K.A.. "ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES", JES. Journal of Engineering Sciences, 36, No 3, 2008, 581-587. doi: 10.21608/jesaun.2008.116136
HARVARD
K.A., A. (2008). 'ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES', JES. Journal of Engineering Sciences, 36(No 3), pp. 581-587. doi: 10.21608/jesaun.2008.116136
VANCOUVER
K.A., A. ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES. JES. Journal of Engineering Sciences, 2008; 36(No 3): 581-587. doi: 10.21608/jesaun.2008.116136
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