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Combining the Global and Local Estimation Models for Predicting PM\(_{10}\) Concentrations

  • Han Bin Bae
  • Tae Hyun Kim
  • Rhee Man KilEmail author
  • Hee Yong Youn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

This paper presents a new way of predicting timely air pollution measure such as the PM\(_{10}\) concentration in Seoul based on a new method of combining the global and local estimation models. In the proposed method, the structure of nonlinear dynamics of generating air pollution data series is analyzed by investigating the attractors in the phase space and this structure is used to build the prediction model. Then, the global estimation model such as the network with Gaussian kernel functions is trained for the air pollution series data. Furthermore, the local estimation model which will recover the errors of the global estimation model using the on-line adaptation method, is also adopted. As a result, the proposed prediction model combining the global and local estimation models provides robust performances of predicting PM\(_{10}\) concentrations.

Keywords

PM\(_{10}\) concentration Phase space analysis Time series prediction Gaussian kernel functions Global and local estimation models 

Notes

Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. B0717-17-0070).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Han Bin Bae
    • 1
  • Tae Hyun Kim
    • 1
  • Rhee Man Kil
    • 1
    Email author
  • Hee Yong Youn
    • 1
  1. 1.College of SoftwareSungkyunkwan UnivesityJangan-gu, Suwon-siKorea

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