IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v107y2021i3d10.1007_s11069-021-04504-3.html
   My bibliography  Save this article

Bayesian network reasoning and machine learning with multiple data features: air pollution risk monitoring and early warning

Author

Listed:
  • Xiaoliang Xie

    (Hunan University of Technology and Business)

  • Jinxia Zuo

    (Hunan University of Technology and Business
    Hunan University of Technology and Business)

  • Bingqi Xie

    (Hunan University of Technology and Business
    Hunan University of Technology and Business)

  • Thomas A. Dooling

    (University of North Carolina At Pembroke)

  • Selvarajah Mohanarajah

    (University of North Carolina At Pembroke)

Abstract

From a macro-perspective, based on machine learning and data-driven approach, this paper utilizes multi-featured data from 31 provinces and regions in China to build a Bayesian network (BN) analysis model for predicting air quality index and warning the air pollution risk at the city level. Further, a two-layer BN for analyzing influencing factors of various air pollutants is developed. Subsequently, the model is applied to forecast the trends of temporal and spatial changes in the form of probabilistic inference and to investigate the degree of impact incurred from individual influencing factors. From the comparisons with the results obtained from other machine learning approaches and algorithms such as neural networks, it is concluded that by comprehensively using the established BN, one can not only reach a monitoring and early warning accuracy rate of 90% but also scrutinize and diagnose the main cause of air pollution risk changes from the perspective of probability.

Suggested Citation

  • Xiaoliang Xie & Jinxia Zuo & Bingqi Xie & Thomas A. Dooling & Selvarajah Mohanarajah, 2021. "Bayesian network reasoning and machine learning with multiple data features: air pollution risk monitoring and early warning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2555-2572, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04504-3
    DOI: 10.1007/s11069-021-04504-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04504-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-04504-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    2. J. Lelieveld & J. S. Evans & M. Fnais & D. Giannadaki & A. Pozzer, 2015. "The contribution of outdoor air pollution sources to premature mortality on a global scale," Nature, Nature, vol. 525(7569), pages 367-371, September.
    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. Bukhari, Ayaz Hussain & Raja, Muhammad Asif Zahoor & Shoaib, Muhammad & Kiani, Adiqa Kausar, 2022. "Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).

    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. Lanzi, Elisa & Dellink, Rob & Chateau, Jean, 2018. "The sectoral and regional economic consequences of outdoor air pollution to 2060," Energy Economics, Elsevier, vol. 71(C), pages 89-113.
    2. Ellen Banzhaf & Sally Anderson & Gwendoline Grandin & Richard Hardiman & Anne Jensen & Laurence Jones & Julius Knopp & Gregor Levin & Duncan Russel & Wanben Wu & Jun Yang & Marianne Zandersen, 2022. "Urban-Rural Dependencies and Opportunities to Design Nature-Based Solutions for Resilience in Europe and China," Land, MDPI, vol. 11(4), pages 1-25, March.
    3. Rogers Kanee & Precious Ede & Omosivie Maduka & Golden Owhonda & Eric Aigbogun & Khalaf F. Alsharif & Ahmed H. Qasem & Shadi S. Alkhayyat & Gaber El-Saber Batiha, 2021. "Polycyclic Aromatic Hydrocarbon Levels in Wistar Rats Exposed to Ambient Air of Port Harcourt, Nigeria: An Indicator for Tissue Toxicity," IJERPH, MDPI, vol. 18(11), pages 1-21, May.
    4. Di Zhang & Xinping Yan & Zaili Yang & Jin Wang, 2014. "An accident data–based approach for congestion risk assessment of inland waterways: A Yangtze River case," Journal of Risk and Reliability, , vol. 228(2), pages 176-188, April.
    5. Stefani Kulebanova & Jana Prodanova & Aleksandra Dedinec & Trifce Sandev & Desheng Wu & Ljupco Kocarev, 2024. "Media Sentiment on Air Pollution: Seasonal Trends in Relation to PM10 Levels," Sustainability, MDPI, vol. 16(15), pages 1-20, July.
    6. Sowmya Malamardi & Katrina A. Lambert & Attahalli Shivanarayanaprasad Praveena & Mahesh Padukudru Anand & Bircan Erbas, 2022. "Time Trends of Greenspaces, Air Pollution, and Asthma Prevalence among Children and Adolescents in India," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    7. Nicholson, Ann E. & Flores, M. Julia, 2011. "Combining state and transition models with dynamic Bayesian networks," Ecological Modelling, Elsevier, vol. 222(3), pages 555-566.
    8. Liu, Haoming & Salvo, Alberto, 2017. "Severe Air Pollution and School Absences: Longitudinal Data on Expatriates in North China," IZA Discussion Papers 11134, Institute of Labor Economics (IZA).
    9. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
    10. Li, Shanjun & Liu, Yanyan & Purevjav, Avralt-Od & Yang, Lin, 2019. "Does subway expansion improve air quality?," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 213-235.
    11. K. K. Shukla & Raju Attada & Aman W. Khan & Prashant Kumar, 2022. "Evaluation of extreme dust storm over the northwest Indo-Gangetic plain using WRF-Chem model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1887-1910, February.
    12. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    13. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    14. Bedoya-Maya, Felipe & Calatayud, Agustina & González Mejia, Vileydy, 2022. "Estimating the effect of urban road congestion on air quality in Latin America," IDB Publications (Working Papers) 12468, Inter-American Development Bank.
    15. Ling-Yun He & Xiao-Feng Qi, 2021. "Environmental Courts, Environment and Employment: Evidence from China," Sustainability, MDPI, vol. 13(11), pages 1-16, June.
    16. Wang, Qiang & Kwan, Mei-Po & Zhou, Kan & Fan, Jie & Wang, Yafei & Zhan, Dongsheng, 2019. "Impacts of residential energy consumption on the health burden of household air pollution: Evidence from 135 countries," Energy Policy, Elsevier, vol. 128(C), pages 284-295.
    17. Weicong Fu & Qunyue Liu & Cecil Konijnendijk van den Bosch & Ziru Chen & Zhipeng Zhu & Jinda Qi & Mo Wang & Emily Dang & Jianwen Dong, 2018. "Long-Term Atmospheric Visibility Trends and Their Relations to Socioeconomic Factors in Xiamen City, China," IJERPH, MDPI, vol. 15(10), pages 1-16, October.
    18. Calvo, Rubén & Álamos, Nicolás & Huneeus, Nicolás & O'Ryan, Raúl, 2022. "Energy poverty effects on policy-based PM2.5 emissions mitigation in southern and central Chile," Energy Policy, Elsevier, vol. 161(C).
    19. Carl-Friedrich Schleussner & Joeri Rogelj & Michiel Schaeffer & Tabea Lissner & Rachel Licker & Erich M. Fischer & Reto Knutti & Anders Levermann & Katja Frieler & William Hare, 2016. "Science and policy characteristics of the Paris Agreement temperature goal," Nature Climate Change, Nature, vol. 6(9), pages 827-835, September.
    20. Wei Xue & Qingming Zhan & Qi Zhang & Zhonghua Wu, 2019. "Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China," IJERPH, MDPI, vol. 17(1), pages 1-23, December.

    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:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04504-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.