Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them...Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems （ANFIS）. To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network （MLP） was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.展开更多
文摘Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems （ANFIS）. To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network （MLP） was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.
