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

  • Date:

    17 April 2019

  • Publication Status:


  • Publisher

    Springer International Publishing

  • DOI:


  • Funders:

    National Natural Science Foundation of China; New Funder


Liu, X., Liu, Q., Zou, Y., & Liu, Q. (2020). A LSTM-Based Approach to Haze Prediction Using a Self-organizing Single Hidden Layer Scheme. In Security with Intelligent Computing and Big-data Services (701-706).



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

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