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Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

Author

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  • Jiangshe Zhang

    (School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China)

  • Weifu Ding

    (School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
    School of Mathematics and Information, BeiFang University of Nationalities, Yinchuan 750021, China)

Abstract

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

Suggested Citation

  • Jiangshe Zhang & Weifu Ding, 2017. "Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong," IJERPH, MDPI, vol. 14(2), pages 1-19, January.
  • Handle: RePEc:gam:jijerp:v:14:y:2017:i:2:p:114-:d:88687
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    References listed on IDEAS

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    1. Keddem, Shimrit & Barg, Frances K. & Glanz, Karen & Jackson, Tara & Green, Sarah & George, Maureen, 2015. "Mapping the urban asthma experience: Using qualitative GIS to understand contextual factors affecting asthma control," Social Science & Medicine, Elsevier, vol. 140(C), pages 9-17.
    2. Mattia Rigotti & Omri Barak & Melissa R. Warden & Xiao-Jing Wang & Nathaniel D. Daw & Earl K. Miller & Stefano Fusi, 2013. "The importance of mixed selectivity in complex cognitive tasks," Nature, Nature, vol. 497(7451), pages 585-590, May.
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    Cited by:

    1. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Yuxuan Ma, 2022. "An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    2. Alisha Banga & Ravinder Ahuja & Subhash Chander Sharma, 2023. "Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 732-745, July.

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