A LSTM-Based Approach to Haze Prediction Using a Self-organizing Single Hidden Layer Scheme

  • Xiaodong Liu
  • Qi LiuEmail author
  • Yanyun Zou
  • Qiang LiuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)


The air quality in urban areas seriously affects the physical and mental health of human beings. And PM2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing haze-fog. Since the meteorological data and air pollutes data are typical time series data, it’s reasonable to adopt a single hidden-layer LSTMNN (Long Short-Term Memory Neural Network) containing memory capability to implement the prediction. As for deciding the best structure of the neural network, this paper employs a self-organizing algorithm, which uses Information Processing Capability (IPC) to adjust the number of the hidden neurons automatically during a learning phase. In a word, to predict PM2.5 concentration accurately, this paper proposes a Self-organizing Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN) to predict PM2.5 concentration. In the experiment, not only the hourly precise prediction but also the daily longer-term prediction is taken into account. At last, the experimental results reflect that SSHL-LSTMNN performs the best.


Haze-fog PM2.5 forecasting Time series data Long Short-Term Memory Neural Network Self-organizing algorithm Information Processing Capability 



This work was supported by Major Program of the National Social Science Fund of China (Grant No. 17ZDA092) and Marie Curie Fellowship (701697-CAR-MSCA-IF-EF-ST).


  1. 1.
    Fuller, G.W., Carslaw, D.C., Lodge, H.W.: An empirical approach for the prediction of daily mean PM10 concentrations. Atmos. Environ. 36(9), 1431–1441 (2002)CrossRefGoogle Scholar
  2. 2.
    Jian, L., Zhao, Y., Zhu, Y.P., Zhan, M.B., Bertolatti, D.: An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou. China Sci. Total. Environ. 426(2), 336–345 (2012)CrossRefGoogle Scholar
  3. 3.
    Liu, D.J., Li, L.: Application study of comprehensive forecasting model based on entropy weighting method on trend of PM2.5 concentration in Guangzhou. China Int. J. Environ. Res. Public Health 12(6), 7085–7099 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhu, H., Lu, X.: The prediction of PM2.5 value based on ARMA and improved bp neural network model. In: 2016 International Conference on Intelligent Networking and Collaborative Systems (ICINCS), pp. 515–517 (2016)Google Scholar
  5. 5.
    Venkadesh, S., Hoogenboom, G., Potter, W.: A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks. Appl. Soft Comput. 13(5), 2253–2260 (2013)CrossRefGoogle Scholar
  6. 6.
    Mishra, D., Goyal, P.: Development of artificial intelligence based NO2, forecasting models at Taj Mahal. Agra. Atmos. Pollut. Res. 6(1), 99–106 (2015)CrossRefGoogle Scholar
  7. 7.
    Li, M., Wang, W., Wang, Z., Xue, Y.: Prediction of PM2.5 concentration based on the similarity in air quality monitoring network. Build. Environ. 137, 11–17 (2018)Google Scholar
  8. 8.
    Mishra, D., Goyal, P., Upadhyay, A.: Artificial intelligence based approach to forecast PM2.5, during haze episodes: a case study of Delhi, India. Atmos. Environ. 102, 239–248 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of ComputingEdinburgh Napier UniversityEdinburghUK
  2. 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)Nanjing University of Information Science and TechnologyNanjingChina
  3. 3.School of ComputerHunan University of TechnologyZhuzhouChina

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