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Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting

Author

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  • Xue-Bo Jin

    (Artificial Intelligence College, 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)

  • Zhong-Yao Wang

    (Artificial Intelligence College, 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)

  • Wen-Tao Gong

    (Artificial Intelligence College, 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)

  • Jian-Lei Kong

    (Artificial Intelligence College, 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)

  • Yu-Ting Bai

    (Artificial Intelligence College, 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)

  • Ting-Li Su

    (Artificial Intelligence College, 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)

  • Hui-Jun Ma

    (Artificial Intelligence College, 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)

  • Prasun Chakrabarti

    (Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, India)

Abstract

Air quality plays a vital role in people’s health, and air quality forecasting can assist in decision making for government planning and sustainable development. In contrast, it is challenging to multi-step forecast accurately due to its complex and nonlinear caused by both temporal and spatial dimensions. Deep models, with their ability to model strong nonlinearities, have become the primary methods for air quality forecasting. However, because of the lack of mechanism-based analysis, uninterpretability forecasting makes decisions risky, especially when the government makes decisions. This paper proposes an interpretable variational Bayesian deep learning model with information self-screening for PM2.5 forecasting. Firstly, based on factors related to PM2.5 concentration, e.g., temperature, humidity, wind speed, spatial distribution, etc., an interpretable multivariate data screening structure for PM2.5 forecasting was established to catch as much helpful information as possible. Secondly, the self-screening layer was implanted in the deep learning network to optimize the selection of input variables. Further, following implantation of the screening layer, a variational Bayesian gated recurrent unit (GRU) network was constructed to overcome the complex distribution of PM2.5 and achieve accurate multi-step forecasting. The high accuracy of the proposed method is verified by PM2.5 data in Beijing, China, which provides an effective way, with multiple factors for PM2.5 forecasting determined using deep learning technology.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:837-:d:1060152
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    References listed on IDEAS

    as
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    2. Guangyuan Xing & Er-long Zhao & Chengyuan Zhang & Jing Wu & Giancarlo Consolo, 2021. "A Decomposition-Ensemble Approach with Denoising Strategy for PM2.5 Concentration Forecasting," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, April.
    3. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
    4. 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.
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    Cited by:

    1. Xin Xu & Cheng-Cai Yang & Yang Xiao & Jian-Lei Kong, 2023. "A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation," IJERPH, MDPI, vol. 20(6), pages 1-20, March.
    2. Yu-Ting Bai & Wei Jia & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong & Zhi-Gang Shi, 2023. "Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    3. Jianlei Kong & Yang Xiao & Xuebo Jin & Yuanyuan Cai & Chao Ding & Yuting Bai, 2023. "LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases," Agriculture, MDPI, vol. 13(11), pages 1-23, October.

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