Advertisement

Prediction of Indoor PM2.5 Index Using Genetic Neural Network Model

  • Hongjie Wu
  • Cheng Chen
  • Weisheng Liu
  • Ru Yang
  • Qiming Fu
  • Baochuan Fu
  • Dadong Dai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

Since people spend more than 80% of the daytime in indoor environment every day, the effect on people’s health of the indoor PM2.5 is much greater than outdoor PM2.5. This paper proposes a method based on genetic neural network to predict the indoor PM2.5. We use seven features including indoor ventilation rate, air temperature, relative humidity and others to train the model. The experiment results showed that the relative error is 5.60%, which is 7.55% lower than the traditional artificial neural network, 5.98% lower than the support vector regression method, 8.36% lower than the Random Forest.

Keywords

Indoor PM2.5 Genetic algorithm Ventilation rate 

Notes

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (61772357, 61502329, 61672371), Jiangsu 333 talent project and top six talent peak project (DZXX-010), Suzhou Foresight Research Project (SYG201704, SNG201610) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0680).

References

  1. 1.
    Ma, Z.W., Hu, X.F.: Estimating ground-level PM2.5 in China using satellite remote sensing. Environ. Sci. Technol. 48(13), 7436–7441 (2014)CrossRefGoogle Scholar
  2. 2.
    Cincinelli, A., Martellini, T.: Indoor air quality and health. Int. J. Environ. Res. Public Health 14(11), 4535–4564 (2017)Google Scholar
  3. 3.
    Phala, K.S.E., Kumar, A., Hancke, G.P.: Air quality monitoring system based on ISO/IEC/IEEE 21451 standards. IEEE Sens. J. 16(12), 5037–5045 (2016)CrossRefGoogle Scholar
  4. 4.
    Hong, B., Qin, H.: Prediction of wind environment and indoor/outdoor relationships for PM2.5 in different building–tree grouping patterns. Atmosphere 9(2), 39–43 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Huang, Y., Yuan, X.: Present situation and development of indoor PM2.5 pollution control. Shanxi Archit. 11(2), 85–90 (2017)Google Scholar
  6. 6.
    Kuang, C.L.: Influence of relative humidity on real-time measurement of indoor PM2.5 concentration. Environ. Sci. Technol. 40(1), 107–111 (2017)Google Scholar
  7. 7.
    Anders, U., Korn, O., Schmitt, C.: Improving the pricing of options: a neural network approach. J. Forecast. 17(5), 369–388 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hongjie Wu
    • 1
    • 2
  • Cheng Chen
    • 1
  • Weisheng Liu
    • 3
  • Ru Yang
    • 1
  • Qiming Fu
    • 1
    • 2
  • Baochuan Fu
    • 1
    • 2
  • Dadong Dai
    • 1
  1. 1.School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
  2. 2.Jiangsu Province Key Laboratory of Intelligent Building Energy EfficiencySuzhou University of Science and TechnologySuzhouChina
  3. 3.Suzhou Municipal Hospital (North Area)SuzhouChina

Personalised recommendations