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Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks

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Abstract

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012–2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

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Acknowledgment

This work was supported by the National Basic Research Program of China (973 Program) under Grant No. 2013CB329404, the Major Research Project of the National Natural Science Foundation of China under Grant No. 91230101, the National Natural Science Foundation of China under Grant Nos. 61075006 and 11201367, the Key Project of the National Natural Science Foundation of China under Grant No.11131006, and the Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20100201120048.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, China

    Weifu Ding & Jiangshe Zhang

  2. School of Mathematics and Information, BeiFang University for Minority, Yinchuan, 750021, China

    Weifu Ding

  3. Institute of Future Cities, Chinese University of Hong Kong, Shatin, Hong Kong

    Yee Leung

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  1. Weifu Ding
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  2. Jiangshe Zhang
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Correspondence to Jiangshe Zhang.

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Ding, W., Zhang, J. & Leung, Y. Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks. Environ Sci Pollut Res 23, 19481–19494 (2016). https://doi.org/10.1007/s11356-016-7149-4

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  • DOI: https://doi.org/10.1007/s11356-016-7149-4

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