Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient

A principal step in designing dividing hydraulic structures entails determining the side weir discharge coefficient. In this study, Firefly optimization‐based Support Vector Regression (SVR‐FF) is introduced and examined in terms of predicting the discharge coefficient of a modified labyrinth side weir. Ten non‐dimensional parameters of various geometrical and hydraulic conditions are defined as the input parameters for the SVR‐FF and the side weir discharge coefficient is defined as the output. Improvements in SVR prediction accuracy are determined by comparing SVR‐FF with the traditional SVR model. The results indicate that the SVR‐FF model with RMSE of 0.035 is about 10% more accurate than SVR with RMSE of 0.039. Thus, combining the Firefly optimization algorithm with SVR increases the prediction model performance.