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

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  • Zaji, Amir Hossein
  • Bonakdari, Hossein
  • Khodashenas, Saeed Reza
  • Shamshirband, Shahaboddin

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.

Suggested Citation

  • Zaji, Amir Hossein & Bonakdari, Hossein & Khodashenas, Saeed Reza & Shamshirband, Shahaboddin, 2016. "Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 14-19.
    • Handle: RePEc:eee:apmaco:v:274:y:2016:i:c:p:14-19
    DOI: 10.1016/j.amc.2015.10.070
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    References listed on IDEAS

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    1. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
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    Cited by:

    1. Meysam Nouri & Parveen Sihag & Ozgur Kisi & Mohammad Hemmati & Shamsuddin Shahid & Rana Muhammad Adnan, 2022. "Prediction of the Discharge Coefficient in Compound Broad-Crested-Weir Gate by Supervised Data Mining Techniques," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    2. Bonakdari, Hossein & Khozani, Zohreh Sheikh & Zaji, Amir Hossein & Asadpour, Navid, 2018. "Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 400-411.
    3. Ram, J. Prasanth & Babu, T. Sudhakar & Rajasekar, N., 2017. "A comprehensive review on solar PV maximum power point tracking techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 826-847.

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