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Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Author

Listed:
  • Thanongsak Xayasouk

    (Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
    These authors contributed equally to this work.)

  • HwaMin Lee

    (Department of Computer Software & Engineering, Soonchunhyang University, Asan 31538, Korea
    These authors contributed equally to this work.)

  • Giyeol Lee

    (Department of Landscape Architecture, Chonnam National University, Gwangju 61186, Korea)

Abstract

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM 10 and PM 2.5 ), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2570-:d:336498
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    References listed on IDEAS

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    1. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
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    Cited by:

    1. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    2. Sang Won Choi & Brian H. S. Kim, 2021. "Applying PCA to Deep Learning Forecasting Models for Predicting PM 2.5," Sustainability, MDPI, vol. 13(7), pages 1-30, March.
    3. 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.
    4. Chih‐Hsuan Wang & Chia‐Rong Chang, 2023. "Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1261-1274, August.
    5. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    6. Fatin Nadiah Yussof & Normah Maan & Mohd Nadzri Md Reba, 2021. "LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    7. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    8. Abdullah H. Al-Nefaie & Theyazn H. H. Aldhyani, 2023. "Predicting CO 2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model," Sustainability, MDPI, vol. 15(9), pages 1-21, May.
    9. Junyoung Jeong & Keuntae Cho, 2024. "Proposing Machine Learning Models Suitable for Predicting Open Data Utilization," Sustainability, MDPI, vol. 16(14), pages 1-23, July.
    10. Hossein Jalali & Farshid Keynia & Faezeh Amirteimoury & Azim Heydari, 2024. "A Short-Term Air Pollutant Concentration Forecasting Method Based on a Hybrid Neural Network and Metaheuristic Optimization Algorithms," Sustainability, MDPI, vol. 16(11), pages 1-17, June.

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