Determination of discharge coefficient of stepped morning glory spillway using a hybrid data-driven method

Abstract

The accurate assessment of the discharge coefficient (Cd) of spillways is one of the complex problems in the safety of dams and reservoirs. In this research, the capabilities of Support Vector Regression-Invasive Weed Optimization (SVR-IWO), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Kernel Ridge Regression (KRR) for estimating discharge coefficients of 21 different layouts of morning glory spillways with the different number of vortex breakers are investigated. In addition, the aleatory and epistemic uncertainties are quantified using classical Mutual information theory and four various Bayesian entropies. The Froud number (Fr), number of vortex breakers (N), and three dimensionless parameters consisting of the water height, vortex height, and breaker thickness over the spillway diameter (H/Ds, h/Ds, and t/Ds, respectively), obtained from 120 experiments, were used as input variables. The findings reveal that SVR-IWO is superior to other models based on several performance metrics, including R, RMSE, MSE, and MAE. Besides, the SVR-IWO (R=0.804,RMSE=0.131,MSE=0.017,MAE=0.074) enhanced the performance indices obtained from standalone SVR (R=0.632,RMSE=0.352,MSE=0.124,MAE=0.255) up to 86.13%.