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Short-Term Prediction of PM 2.5 Using LSTM Deep Learning Methods

Author

Listed:
  • Endah Kristiani

    (Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
    Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia)

  • Hao Lin

    (Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan)

  • Jwu-Rong Lin

    (Department of International Business, Tunghai University, Taichung City 407224, Taiwan)

  • Yen-Hsun Chuang

    (Department of International Business, Tunghai University, Taichung City 407224, Taiwan)

  • Chin-Yin Huang

    (Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung City 407224, Taiwan)

  • Chao-Tung Yang

    (Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
    Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec.4, Taiwan Boulevard, Taichung City 407224, Taiwan)

Abstract

This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O 3 , SO 2 , and CO 2 from 2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model’s accuracy. The average absolute error percentage value was used in the experiments to evaluate the model’s performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value.

Suggested Citation

  • Endah Kristiani & Hao Lin & Jwu-Rong Lin & Yen-Hsun Chuang & Chin-Yin Huang & Chao-Tung Yang, 2022. "Short-Term Prediction of PM 2.5 Using LSTM Deep Learning Methods," Sustainability, MDPI, vol. 14(4), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2068-:d:747235
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    References listed on IDEAS

    as
    1. Thanongsak Xayasouk & HwaMin Lee & Giyeol Lee, 2020. "Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    2. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
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