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.
This is a preview of subscription content, log in via an institution to check access.
References
Gardner, M., Dorling, S.: Artificial neural networks (the multilayer perceptron) - a review of applications in the atmosphere sciences. Atmos. Environ. 32, 2627–2636 (1998)
Patricio, P., Alex, T., Jorge, R.: Prediction of PM\(_{2.5}\) concentrations several hours in advance using neural networks in Santiago, Chile. Atmos. Environ. 34, 1189–1196 (2000)
Jiang, D., Zhang, Y., Hu, X., Zeng, Y., Tan, J., Shao, D.: Progress in developing an ANN model for air pollution index forecast. Atmos. Environ. 38, 7055–7064 (2004)
Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., Brasseur, O.: A neural network forecast for daily average PM\(_{10}\) concentrations in Belgium. Atmos. Environ. 39, 3279–3289 (2005)
Grivas, G., Chaloulakou, A.: Artificial neural network models for prediction of PM\(_{10}\) hourly concentrations in the greater area of Athens, Greece. Atmos. Environ. 40, 1216–1229 (2006)
Cao, Z., Yu, S., Xu G., Chen B., Principe J.: Multiple adaptive kernel size KLMS for Beijing PM2.5 prediction. In: International Joint Conference on Neural Networks, pp. 1403–1407 (2016)
Kil, R., Park, S., Kim, S.: Time series analysis based on the smoothness measure of mapping in the phase space of attractors. In: International Joint Conference on Neural Networks, vol. 4, pp. 2584–2589 (1999)
Kil, R.: Function approximation based on a network with kernel functions of bounds and locality: an approach of non-parametric estimation. ETRI J. 15, 35–51 (1993)
Kim, D.K., Kil, R.M.: Stock price prediction based on a network with Gaussian kernel functions. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8227, pp. 705–712. Springer, Heidelberg (2013). doi:10.1007/978-3-642-42042-9_87
Air Korea, PM\(_{10}\) and PM\(_{2.5}\) Concentrations Information on South Korea. http://www.airkorea.or.kr/
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. B0717-17-0070).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bae, H.B., Kim, T.H., Kil, R.M., Youn, H.Y. (2017). Combining the Global and Local Estimation Models for Predicting PM\(_{10}\) Concentrations. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-70139-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70138-7
Online ISBN: 978-3-319-70139-4
eBook Packages: Computer ScienceComputer Science (R0)