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Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

Author

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  • Xue-Bo Jin

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Nian-Xiang Yang

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Xiao-Yi Wang

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Yu-Ting Bai

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Ting-Li Su

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Jian-Lei Kong

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

Abstract

Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

Suggested Citation

  • Xue-Bo Jin & Nian-Xiang Yang & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction," Mathematics, MDPI, vol. 8(2), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:214-:d:317864
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    References listed on IDEAS

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    1. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    2. Oluwafadekemi Aero & Adeyemi A. Ogundipe, 2018. "Fiscal Deficit and Economic Growth in Nigeria: Ascertaining a Feasible Threshold," International Journal of Economics and Financial Issues, Econjournals, vol. 8(3), pages 296-306.
    3. Yuting Bai & Xuebo Jin & Xiaoyi Wang & Tingli Su & Jianlei Kong & Yutian Lu, 2019. "Compound Autoregressive Network for Prediction of Multivariate Time Series," Complexity, Hindawi, vol. 2019, pages 1-11, September.
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    Cited by:

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    2. Wongchai, Anupong & Jenjeti, Durga rao & Priyadarsini, A. Indira & Deb, Nabamita & Bhardwaj, Arpit & Tomar, Pradeep, 2022. "Farm monitoring and disease prediction by classification based on deep learning architectures in sustainable agriculture," Ecological Modelling, Elsevier, vol. 474(C).
    3. Ying Shu & Chengfu Ding & Lingbing Tao & Chentao Hu & Zhixin Tie, 2023. "Air Pollution Prediction Based on Discrete Wavelets and Deep Learning," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
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    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. Tao Zhen & Lei Yan & Jian-lei Kong, 2020. "An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection," IJERPH, MDPI, vol. 17(16), pages 1-17, August.
    7. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    8. Junbeom Park & Seongju Chang, 2021. "A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network," IJERPH, MDPI, vol. 18(13), pages 1-15, June.
    9. Mei-Hsin Chen & Yao-Chung Chen & Tien-Yin Chou & Fang-Shii Ning, 2023. "PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework," IJERPH, MDPI, vol. 20(5), pages 1-13, February.

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    Keywords

    PM2.5 data; air pollution prediction; EMD; CNN; GRU;
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