IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p837-d1060152.html
   My bibliography  Save this article

Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/837/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/4/837/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
    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. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    2. 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.
    3. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    4. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    5. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    6. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    7. Krishna Prakash N. & Jai Govind Singh, 2023. "Electricity price forecasting using hybrid deep learned networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1750-1771, November.
    8. Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
    9. 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.
    10. Wang, Zhihong & Luo, Kangwei & Yu, Hongsen & Feng, Kai & Ding, Hang, 2024. "NOx Emission prediction of heavy-duty diesel vehicles based on Bayesian optimization -Gated Recurrent Unit algorithm," Energy, Elsevier, vol. 292(C).
    11. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Min Zuo & Qing-Chuan Zhang & Seng Lin, 2021. "Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse," Agriculture, MDPI, vol. 11(8), pages 1-25, August.
    12. Wang, Jianxing & Guo, Lili & Zhang, Chengying & Song, Lei & Duan, Jiangyong & Duan, Liqiang, 2020. "Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method," Energy, Elsevier, vol. 208(C).
    13. Ali Asghar Heidari & Mehdi Akhoondzadeh & Huiling Chen, 2022. "A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-35, September.
    14. 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.
    15. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
    16. Xudong Pang & Xiangchen Lu & Hao Ding & Josep M. Guerrero, 2022. "Construction of Smart Grid Load Forecast Model by Edge Computing," Energies, MDPI, vol. 15(9), pages 1-16, April.
    17. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    18. Krzysztof Lalik & Filip Wątorek, 2021. "Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles," Energies, MDPI, vol. 14(22), pages 1-18, November.
    19. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    20. Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024. "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 186-194, July.

    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:jmathe:v:11:y:2023:i:4:p:837-:d:1060152. 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.