Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir’s discharge coefficient. (English) Zbl 1410.90286
Summary: 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.
MSC:
| 90C59 | Approximation methods and heuristics in mathematical programming |
Keywords:
discharge coefficient; firefly optimization algorithm; modified labyrinth side weir; neural network; support vector regressionReferences:
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