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A deep multitask learning approach for air quality prediction

Author

Listed:
  • Xiaotong Sun

    (Renmin University of China)

  • Wei Xu

    (Renmin University of China)

  • Hongxun Jiang

    (Renmin University of China)

  • Qili Wang

    (Renmin University of China)

Abstract

Air pollution is one of the most serious threats to human health and is an issue causing growing public concern. Air quality forecasts play a fundamental role in providing decision-making support for environmental governance and emergency management, and there is an imperative need for more accurate forecasts. In this paper, we propose a novel spatial–temporal deep multitask learning (ST-DMTL) framework for air quality forecasting based on dynamic spatial panels of multiple data sources. Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records that were generated on an hourly basis from January 2013 to September 2017 in China. An application check is also conducted to verify the practical value of our proposed ST-DMTL framework. Our empirical results indicate the efficacy of the framework as a viable approach for air quality forecasts.

Suggested Citation

  • Xiaotong Sun & Wei Xu & Hongxun Jiang & Qili Wang, 2021. "A deep multitask learning approach for air quality prediction," Annals of Operations Research, Springer, vol. 303(1), pages 51-79, August.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-020-03734-1
    DOI: 10.1007/s10479-020-03734-1
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    References listed on IDEAS

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    1. Nur Rahman & Muhammad Lee & Suhartono & Mohd Latif, 2015. "Artificial neural networks and fuzzy time series forecasting: an application to air quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2633-2647, November.
    2. Javad Khazaei & Anthony Downward & Golbon Zakeri, 2014. "Modelling counter-intuitive effects on cost and air pollution from intermittent generation," Annals of Operations Research, Springer, vol. 222(1), pages 389-418, November.
    3. Ferretti, Valentina & Montibeller, Gilberto, 2016. "Key challenges and meta-choices in designing and applying multi-criteria spatial decision support systems," LSE Research Online Documents on Economics 65368, London School of Economics and Political Science, LSE Library.
    4. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    5. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
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    Cited by:

    1. Tsan-Ming Choi & Alexandre Dolgui & Dmitry Ivanov & Erwin Pesch, 2022. "OR and analytics for digital, resilient, and sustainable manufacturing 4.0," Annals of Operations Research, Springer, vol. 310(1), pages 1-6, March.
    2. Jing Ma & Xiaoyu Guo & Xufeng Zhao, 2024. "Identifying purchase intention through deep learning: analyzing the Q &D text of an E-Commerce platform," Annals of Operations Research, Springer, vol. 339(1), pages 329-348, August.
    3. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2024. "Learning fused lasso parameters in portfolio selection via neural networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4281-4299, October.

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