IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2068-d747235.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2068/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2068/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).
    3. 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.
    4. 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.
    5. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    6. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    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. Yagli, Gokhan Mert & Yang, Dazhi & Gandhi, Oktoviano & Srinivasan, Dipti, 2020. "Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?," Applied Energy, Elsevier, vol. 259(C).
    9. 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.
    10. 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.
    11. Yeom, Jong-Min & Deo, Ravinesh C. & Adamwoski, Jan F. & Chae, Taebyeong & Kim, Dong-Su & Han, Kyung-Soo & Kim, Do-Yong, 2020. "Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    12. 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.
    13. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    14. 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.
    15. Chuanjie Xie & Chong Huang & Deqiang Zhang & Wei He, 2021. "BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data," IJERPH, MDPI, vol. 18(19), pages 1-12, September.
    16. Houtian Tang & Yuanlai Wu & Jinxiu Chen & Liuxin Deng & Minjie Zeng, 2022. "How Does Change in Rural Residential Land Affect Cultivated Land Use Efficiency? An Empirical Study Based on 42 Cities in the Middle Reaches of the Yangtze River," Land, MDPI, vol. 11(12), pages 1-20, December.
    17. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Missing data imputation using mixture factor analysis for building electric load data," Applied Energy, Elsevier, vol. 304(C).
    18. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
    19. Sascha Lindig & Atse Louwen & David Moser & Marko Topic, 2020. "Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches," Energies, MDPI, vol. 13(19), pages 1-18, September.
    20. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2068-:d:747235. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.