IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p4889-d796937.html
   My bibliography  Save this article

Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Author

Listed:
  • Yuan Huang

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Junhao Yu

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Xiaohong Dai

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Zheng Huang

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

  • Yuanyuan Li

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China)

Abstract

Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and health of China’s people. However, the diversity and complexity of the factors which affect air pollution render air quality monitoring data complex and nonlinear. To improve the accuracy of prediction of the air quality index (AQI) and obtain more accurate AQI data with respect to their nonlinear and nonsmooth characteristics, this study introduces an air quality prediction model based on the empirical mode decomposition (EMD) of LSTM and uses improved particle swarm optimization (IPSO) to identify the optimal LSTM parameters. First, the model performed the EMD decomposition of air quality data and obtained uncoupled intrinsic mode function (IMF) components after removing noisy data. Second, we built an EMD–IPSO–LSTM air quality prediction model for each IMF component and extracted prediction values. Third, the results of validation analyses of the algorithm showed that compared with LSTM and EMD–LSTM, the improved model had higher prediction accuracy and improved the model fitting effect, which provided theoretical and technical support for the prediction and management of air pollution.

Suggested Citation

  • Yuan Huang & Junhao Yu & Xiaohong Dai & Zheng Huang & Yuanyuan Li, 2022. "Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4889-:d:796937
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/4889/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/4889/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haibo Liu & Yujie Dong & Fuzhong Wang, 2020. "Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    2. Jianxian Cai & Xun Dai & Li Hong & Zhitao Gao & Zhongchao Qiu, 2020. "An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
    3. Cabaneros, Sheen Mclean & Calautit, John Kaiser & Hughes, Ben, 2020. "Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique," Ecological Modelling, Elsevier, vol. 424(C).
    4. Meng Dun & Zhicun Xu & Yan Chen & Lifeng Wu, 2020. "Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, May.
    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. Li, Naiqing & Li, Longhao & Zhang, Fan & Jiao, Ticao & Wang, Shuang & Liu, Xuefeng & Wu, Xinghua, 2023. "Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model," Energy, Elsevier, vol. 277(C).
    2. Li Yang & Xin Fang & Xue Wang & Shanshan Li & Junqi Zhu, 2022. "Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm," IJERPH, MDPI, vol. 19(19), pages 1-18, September.
    3. Wenbing Chang & Xu Chen & Zhao He & Shenghan Zhou, 2023. "A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods," Sustainability, MDPI, vol. 15(22), pages 1-24, November.
    4. Junqi Zhu & Haotian Zheng & Li Yang & Shanshan Li & Liyan Sun & Jichao Geng, 2023. "Evaluation of deep coal and gas outburst based on RS-GA-BP," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(3), pages 2531-2551, February.
    5. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    6. Axel Gedeon Mengara Mengara & Eunyoung Park & Jinho Jang & Younghwan Yoo, 2022. "Attention-Based Distributed Deep Learning Model for Air Quality Forecasting," Sustainability, MDPI, vol. 14(6), pages 1-34, March.
    7. Pei Yin & Jing Cheng & Miaojuan Peng, 2022. "Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    8. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    9. Xue-Bo Jin & Zhong-Yao Wang & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2023. "Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting," Mathematics, MDPI, vol. 11(4), pages 1-18, February.

    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:9:p:4889-:d:796937. 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.