Estimation of triangular side orifice discharge coefficient under a free flow condition using data-driven models

In irrigation channel system, diverse side orifice shapes (e.g., circular, rectangular, or triangular) are widely designed for flow control and regulation. Hence, accurate estimating the discharge diverted from the main channel to the side one is an essential for water management. The main objective of this research is to estimate the discharge coefficient (C_d) for a sharp-crested triangular side orifice under a free flow condition. Three linear data-driven models including locally weighted learning regression (LWLR), multiple linear regressions with interaction (MLRI), and multivariate linear regression (MLR) were developed for this purpose. 570 experimental datasets were used to build the predictive models. Two modeling scenarios (with and without incorporating the upstream flow Froude number) were investigated for estimating C_d. The best input combinations for both scenarios were identified by applying the Gamma test approach. The performance of the models was assessed by using various graphical analysis and statistical metrics. The modeling results indicated that LWLR and MLRI had similar good performance for both modeling scenarios and provided accurate estimation of the C_d values. Overall, this study demonstrated the capacities of the data-driven models in estimating the discharge coefficient of triangular side orifice under a free flow condition.