The effects of parameters such as weir length, height of weir, water depth at weir entrance and bed slope on the discharge coefficient of a rectangular sharp-crested side weir were analysed using standard linear regression and the adaptive neuro-fuzzy inference system (Anfis). The results showed that the Anfis model performed better than the regression model in all different scenarios defined in this study. Anfis can simulate the discharge coefficient better than the regression model and always works better. Without considering the channel slope, both models represent satisfactory results, but the Anfis model shows more sensitivity to channel slope than does the regression model. Based on the results of this study, the Anfis model is suggested for estimation of side weir discharge coefficient.
NotationFroude number at upstream end of side weir dQ/ds discharge per unit length of side weir ((m 3 /s)/m) g acceleration due to gravity (m/s 2 ) L length of side weir (m) p height of weir crest (m) Q discharge in main channel (m 3 /s) Q 1 discharge in main channel at upstream end of side weir (m 3 /s) Q 2 discharge in main channel at downstream end of side weir (m 3 /s) q discharge per unit length over side weir ((m 3 /s)/m) S 0 channel slope s distance along side weir measured from upstream end of side weir (m) V 1 mean velocity of flow at upstream end of side weir (m/s) y flow depth measured from the channel bottom (m) y 1 flow depth at upstream end of side weir at channel centre (m) y 2 flow depth at downstream end of side weir at channel centre (m)
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