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A Novel HPSOSA for Kernel Function Type and Parameter Optimization of SVR in Rainfall Forecasting

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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Abstract

In this paper, a novel co-evolution algorithm is presented to optimize the type of kernel function and the kernel parameter setting of Support Vector Regression (SVR) for rainfall prediction based on hybrid Particle Swarm Optimization and Simulated Annealing (HPSOSA), namely HPSOSA-SVR. The HPSOSA algorithm is carried out the metropolis process of SA into the movement mechanism and parallel processing of PSO. By combining the two methods, the HPSOSA algorithm has the advantage of both fast calculation and searching in the direction of the global optimum solution, helping PSO jump out of local optima, avoiding into the local optimal solution early and leading to a good solution quality. The developed HPSOSA-SVR model is being applied for monthly rainfall forecasting. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements.

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Acknowledgments

This work was supported the Natural Science Foundation of Guangxi Province under Grant No. 2014GXNSFAA118027, and by the Guangxi Education Department under Grant YB2014467, and by the Key Laboratory for Mixed and Missing Data Statistics of the Education Department of Guangxi Province under Grant No. GXMMSL201405, and by the Key Disciplines for Operational Research and Cybernetics of the Education Department of Guangxi Province.

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Authors and Affiliations

  1. Department of Mathematics and Computer, Guangxi Science and Technology, Normal University, Laibin, 546199, Guangxi, China

    Jiansheng Wu

  2. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, People’s Republic of China

    Jiansheng Wu

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  1. Jiansheng Wu
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Correspondence to Jiansheng Wu .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Wu, J. (2017). A Novel HPSOSA for Kernel Function Type and Parameter Optimization of SVR in Rainfall Forecasting. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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