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A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

Author

Listed:
  • Wenbing Chang

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Xu Chen

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Zhao He

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Shenghan Zhou

    (School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

Abstract

Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques to reveal complex relationships in massive knowledge from multiple sources for correct air quality prediction. This paper proposes a prediction hybrid framework for air quality integrated with W-BiLSTM(PSO)-GRU and XGBoost methods. Exploiting the potential of wavelet decomposition and PSO parameter optimization, the prediction accuracy, stability and robustness was improved. The results indicate that the R 2 values of PM2.5, PM10, SO 2 , CO, NO 2 , and O 3 predictions exceeded 0.94, and the MAE and RMSE values were lower than 0.02 and 0.03, respectively. By integrating the state-of-the-art XGBoost algorithm, meteorological data from neighboring monitoring stations were taken into account to predict air quality trends, resulting in a wider range of forecasts. This strategic merger not only enhanced the prediction accuracy, but also effectively solved the problem of sudden interruption of monitoring. Rigorous analysis and careful experiments showed that the proposed method is effective and has high application value in air quality prediction, building a solid framework for informed decision-making and sustainable development policy formulation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16064-:d:1282528
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    References listed on IDEAS

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    1. Chelladurai Aarthi & Varatharaj Jeya Ramya & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari, 2023. "Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    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. Changshun Li & Ziyang Xie & Bo Chen & Kaijin Kuang & Daowei Xu & Jinfu Liu & Zhongsheng He, 2021. "Different Time Scale Distribution of Negative Air Ions Concentrations in Mount Wuyi National Park," IJERPH, MDPI, vol. 18(9), pages 1-15, May.
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