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

Amir Hossein Zaji, Hossein Bonakdari, Saeed Reza Khodashenas and Shahaboddin Shamshirband ()

Applied Mathematics and Computation, 2016, vol. 274, issue C, 14-19

Abstract: 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.

Keywords: Discharge coefficient; Firefly optimization algorithm; Modified labyrinth side weir; Neural network; Support vector regression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:274:y:2016:i:c:p:14-19

DOI: 10.1016/j.amc.2015.10.070

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