Resumen
In this study, the energy dissipation of cascade spillways was studied by conducting a series of laboratory experiments. Five spillways slope angles (a) (10°, 20°, 30°, 40°, and 50°), various step numbers (N) ranging from 4 to 75, and a wide range of discharges (Q), were considered. Some data-based models were developed to explain the relationships between hydraulic parameters. Multiple linear and nonlinear regression-based equations were developed based on dimensional analysis theory to compute energy dissipation over cascade spillways. For testing the robustness of developed data-based models, M5P, stochastic M5P, and random forest (RF) were used as new artificial intelligence (AI)-based techniques. To relate the input and output variables of energy dissipation, AI-based and regression approaches were developed. It was found that the formulation based on the stochastic M5P approach in solving energy dissipation problems over cascade spillways is more successful than the other regression and AI-based methods. Sensitivity analysis suggests that spillway slope in degrees (a) is the most influential input variable in predicting the relative energy dissipation (%) of the spillway in comparison to other input variables.