ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES
K.A., A. (2008). ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES. EKB Journal Management System, 36(No 3), 581-587. doi: 10.21608/jesaun.2008.116136
Amen, K.A.. "ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES". EKB Journal Management System, 36, No 3, 2008, 581-587. doi: 10.21608/jesaun.2008.116136
K.A., A. (2008). 'ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES', EKB Journal Management System, 36(No 3), pp. 581-587. doi: 10.21608/jesaun.2008.116136
K.A., A. ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES. EKB Journal Management System, 2008; 36(No 3): 581-587. doi: 10.21608/jesaun.2008.116136

ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DISCHARGE BELOW GATES

JES. Journal of Engineering Sciences
Article 2, Volume 36, No 3, May and June 2008, Page 581-587  XML PDF (430.05 K)
Document Type: Research Paper
DOI: 10.21608/jesaun.2008.116136
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Author
Amen, K.A.
Civil Engineering Department, Faculty of Engineering, Assiut University
Abstract
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.
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