IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2546-d1612015.html
   My bibliography  Save this article

A New Hybrid Deep Sequence Model for Decomposing, Interpreting, and Predicting Sulfur Dioxide Decline in Coastal Cities of Northern China

Author

Listed:
  • Guoju Wang

    (School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Shandong Engineering Research Center for Marine Scenarized Application of Artificial Intelligence, Qingdao University of Science and Technology, Qingdao 266061, China
    Qingdao Technology Innovation Center of Artificial Intelligence Oceanography, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Rongjie Zhu

    (Tandon School of Engineering, New York University, Brooklyn, NY 10012, USA)

  • Xiang Gong

    (School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Shandong Engineering Research Center for Marine Scenarized Application of Artificial Intelligence, Qingdao University of Science and Technology, Qingdao 266061, China
    Qingdao Technology Innovation Center of Artificial Intelligence Oceanography, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Xiaoling Li

    (School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Shandong Engineering Research Center for Marine Scenarized Application of Artificial Intelligence, Qingdao University of Science and Technology, Qingdao 266061, China
    Qingdao Technology Innovation Center of Artificial Intelligence Oceanography, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Yuanzheng Gao

    (School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Shandong Engineering Research Center for Marine Scenarized Application of Artificial Intelligence, Qingdao University of Science and Technology, Qingdao 266061, China
    Qingdao Technology Innovation Center of Artificial Intelligence Oceanography, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Wenming Yin

    (School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
    Shandong Engineering Research Center for Marine Scenarized Application of Artificial Intelligence, Qingdao University of Science and Technology, Qingdao 266061, China
    Qingdao Technology Innovation Center of Artificial Intelligence Oceanography, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Renzheng Wang

    (College of Environmental Science and Engineering, Ocean University of China, Qingdao 266071, China)

  • Huan Li

    (National Marine Data and Information Service, Ministry of Natural Resources, Tianjin 300171, China)

  • Huiwang Gao

    (Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ocean University of China, Qingdao 266071, China
    Laboratory for Marine Ecology and Environmental Science, Qingdao Marine Science and Technology Center, Qingdao 266071, China)

  • Tao Zou

    (Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China)

Abstract

The recent success of emission reduction policies in China has significantly lowered sulfur dioxide (SO 2 ) levels. However, accurately forecasting these concentrations remains challenging due to their inherent non-stationary tendency. This study introduces an innovative hybrid deep learning model, RF-VMD-Seq2Seq, combining the Random Forest (RF) algorithm, Variational Mode Decomposition (VMD), and the Sequence-to-Sequence (Seq2Seq) framework to improve SO 2 concentration forecasting in five coastal cities of northern China. Our results show that the predicted SO 2 concentrations closely align with observed values, effectively capturing fluctuations, outliers, and extreme events—such as sharp declines the Novel Coronavirus Pneumonia (COVID-19) pandemic in 2020—along with the upper 5% of SO 2 levels. The model achieved high coefficients of determination (>0.91) and Pearson’s correlation (>0.96), with low prediction errors (RMSE < 1.35 μg/m 3 , MAE < 0.94 μg/m 3 , MAPE < 15%). The low-frequency band decomposing from VMD showed a notable long-term decrease in SO 2 concentrations from 2013 to 2020, with a sharp decline since 2018 during heating seasons, probably due to the ‘Coal-to-Natural Gas’ policy in northern China. The input sequence length of seven steps was recommended for the prediction model, based on high-frequency periodicities extracted through VMD, which significantly improved our model performance. This highlights the critical role of weekly-cycle variations in SO 2 levels, driven by anthropogenic activities, in enhancing the accuracy of one-day-ahead SO 2 predictions across northern China’s coastal regions. The results of the RF model further reveal that CO and NO 2 , sharing common anthropogenic sources with SO 2 , contribute over 50% to predicting SO 2 concentrations, while meteorological factors—relative humidity (RH) and air temperature—contribute less than 20%. Additionally, the integration of VMD outperformed both the standard Seq2Seq and Ensemble Empirical Mode Decomposition (EEMD)-enhanced Seq2Seq models, showcasing the advantages of VMD in predicting SO 2 decline. This research highlights the potential of the RF-VMD-Seq2Seq model for non-stationary SO 2 prediction and its relevance for environmental protection and public health management.

Suggested Citation

  • Guoju Wang & Rongjie Zhu & Xiang Gong & Xiaoling Li & Yuanzheng Gao & Wenming Yin & Renzheng Wang & Huan Li & Huiwang Gao & Tao Zou, 2025. "A New Hybrid Deep Sequence Model for Decomposing, Interpreting, and Predicting Sulfur Dioxide Decline in Coastal Cities of Northern China," Sustainability, MDPI, vol. 17(6), pages 1-30, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2546-:d:1612015
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2546/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/6/2546/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    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. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    2. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    3. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    4. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    5. Mohamed Zine & Fouzi Harrou & Mohammed Terbeche & Mohammed Bellahcene & Abdelkader Dairi & Ying Sun, 2023. "E-Learning Readiness Assessment Using Machine Learning Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    6. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    7. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    8. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    9. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    10. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    11. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    12. Ilias Thomas & Alex M. Dickens & Jussi P. Posti & Endre Czeiter & Daniel Duberg & Tim Sinioja & Matilda Kråkström & Isabel R. A. Retel Helmrich & Kevin K. W. Wang & Andrew I. R. Maas & Ewout W. Steyer, 2022. "Serum metabolome associated with severity of acute traumatic brain injury," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. Lu, Xuefei & Baraldi, Piero & Zio, Enrico, 2020. "A data-driven framework for identifying important components in complex systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    14. Mahyar Jahaninasab & Ehsan Taheran & S. Alireza Zarabadi & Mohammadreza Aghaei & Ali Rajabpour, 2023. "A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers," Energies, MDPI, vol. 16(13), pages 1-13, July.
    15. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    16. Jianghong Xu & Wei Lu & Weixin Wang, 2024. "From “fragile smallholders” to “resilient smallholders”: measuring rural household resilience in China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    17. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    18. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    19. Salcedo-Sanz, S. & Pastor-Sánchez, A. & Del Ser, J. & Prieto, L. & Geem, Z.W., 2015. "A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction," Renewable Energy, Elsevier, vol. 75(C), pages 93-101.
    20. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:6:p:2546-:d:1612015. 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.