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Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania

Author

Listed:
  • Svajone Bekesiene

    (Research Group on Logistics and Defence Technology Management, General Jonas Zemaitis Military Academy of Lithuania, Silo 5A, 10322 Vilnius, Lithuania)

  • Ieva Meidute-Kavaliauskiene

    (Research Group on Logistics and Defence Technology Management, General Jonas Zemaitis Military Academy of Lithuania, Silo 5A, 10322 Vilnius, Lithuania)

Abstract

This study focuses on the Vilnius (capital of Lithuania) agglomeration, which is facing the issue of air pollution resulting from the city’s physical expansion. The increased number of industries and vehicles caused an increase in the rate of fuel consumption and pollution in Vilnius, which has rendered air pollution control policies and air pollution management more significant. In this study, the differences in the pollutants’ means were tested using two-sided t -tests. Additionally, a 2-layer artificial neural network and a pollution data were both used as tools for predicting and warning air pollution after loop traffic has taken effect in Vilnius Old Town from July of 2020. Highly accurate data analysis methods provide reliable data for predicting air pollution. According to the validation, the multilayer perceptron network (MLPN1), with a hyperbolic tangent activation function with a 4-4-2 partition, produced valuable results and identified the main pollutants affecting and predicting air quality in the Old Town: maximum concentration of sulphur dioxide per 1 hour (SO 2 _1 h, normalized importance = 100%); carbon monoxide (CO) was the second pollutant with the highest indication of normalized importance, equalling 59.0%.

Suggested Citation

  • Svajone Bekesiene & Ieva Meidute-Kavaliauskiene, 2022. "Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2470-:d:754768
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    References listed on IDEAS

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    2. David Levinson & David Gillen & Adib Kanafani, 1998. "The social costs of intercity transportation: a review and comparison of air and highway," Working Papers 199801, University of Minnesota: Nexus Research Group.
    3. Svajone Bekesiene & Ieva Meidute-Kavaliauskiene & Vaida Vasiliauskiene, 2021. "Accurate Prediction of Concentration Changes in Ozone as an Air Pollutant by Multiple Linear Regression and Artificial Neural Networks," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
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