Affiliation:
1. Department of Hydraulic Engineering, College of Civil Engineering and Architecture Zhejiang University Hangzhou China
2. State Key Laboratory of Eco‐hydraulics in Northwest Arid Region of China Xi'an University of Technology Xi'an China
3. Faculty of Water Sciences Engineering Shahid Chamran University of Ahvaz Ahvaz Iran
Abstract
AbstractThe broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and extreme learning machine (ELM) are used to predict the discharge coefficient (Cd) of the triangular broad‐crested weir. The comprehensive analysis shows that the ELM model has high stability, predictive ability, and computational speed. The coefficient of determination (R^2) is 0.99982, the mean absolute percentage error (MAPE) is 0.000261, the Nash‐Sutcliffe coefficient (NSE) is 0.99977, and the root means square error (RMSE) is 4.17E‐05 in the testing phase. The apex angle θ is the most critical parameter affecting the Cd, and the contribution to the Cd is 52.45%. A new computational formula is proposed and compared with the accuracy of empirical formulas, showing that the intelligent method has higher accuracy and efficiency.