Advanced computational intelligence for accurate discharge coefficient prediction in triangular and rectangular slit weirs

ABSTRACT Reliable discharge estimation is fundamental to the design and operation of hydraulic structures like weirs, which are critical for irrigation, flood control, and water treatment. Slit weirs offer an efficient solution for precise flow management with minimal energy loss, yet accurately predicting their discharge coefficient (Cd) remains a persistent challenge, particularly for geometrically complex designs such as triangular slit weirs. This paper addresses this challenge by, first, providing a comprehensive characterization of triangular slit weir discharge behavior and proposing a new, highly accurate empirical equation for their Cd. Second, and critically, this study explores the power of advanced computational intelligence to enhance Cd and discharge prediction for both rectangular and triangular slit weirs. It study systematically compares the performance of Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Machines (SVM), and traditional regression models. These findings reveal that the ANN model achieves outstanding performance (Training R2 = 0.98, Testing R2 = 0.88), surpassing ANFIS (Testing R2 = 0.86), SVM (Testing R2 = 0.85), and regression models (Testing R2 = 0.82).