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Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution

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  • Bukhari, Ayaz Hussain
  • Raja, Muhammad Asif Zahoor
  • Shoaib, Muhammad
  • Kiani, Adiqa Kausar

Abstract

Air Pollution is an emerging disaster and considered one of the biggest challenges of the world to effectively control, mitigate and forecast due to abrupt variability, stochastic, and chaotic pattern of particulate matter (PM) in terms of time and space of the pollutants. Composition of ambient PM not only causes serious damage to public health but also emerging as a global hazard particularly for urban environment with negative impact on human health including morbidity. Mortality and ultimately towards unstable economy. In this study, hourly short-term trends of PM2.5 and air quality index (AQI) of Lahore city of Pakistan is monitored and mitigated by the design of fractional order Lorenz based physics informed hybrid computing paradigm SARFIMA-NARX for forecasting hourly pattern of next two days. The complex dynamics of earth system and its weather forecast are characterized by combination of biological, physical, and chemical processes governed by the different laws of science that provides additional information for the climate variation in terms of physics inform intelligence. The performance index based on statistical indicator of RMSE confirmed the high accuracy and efficiency of designed model to predict the pattern. The early predictions based on computational intelligence paradigm may serve as a surveillance system to reduce the air pollution through cost-effectiveness planning by environmental monitoring agencies.

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  • 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).
    • Handle: RePEc:eee:chsofr:v:161:y:2022:i:c:s0960077922005859
    DOI: 10.1016/j.chaos.2022.112375
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    2. Avcı, İbrahim & Lort, Hüseyin & Tatlıcıoğlu, Buğce E., 2023. "Numerical investigation and deep learning approach for fractal–fractional order dynamics of Hopfield neural network model," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Yoshioka, Hidekazu & Yoshioka, Yumi, 2024. "Assessing fluctuations of long-memory environmental variables based on the robustified dynamic Orlicz risk," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    4. Flavia Matias Oliveira Silva & Eduardo Carlos Alexandrina & Ana Cristina Pardal & Maria Teresa Carvalhos & Elaine Schornobay Lui, 2022. "Monitoring and Prediction of Particulate Matter (PM 2.5 and PM 10 ) around the Ipbeja Campus," Sustainability, MDPI, vol. 14(24), pages 1-9, December.
    5. Anwar, Nabeela & Ahmad, Iftikhar & Kiani, Adiqa Kausar & Shoaib, Muhammad & Raja, Muhammad Asif Zahoor, 2024. "Novel intelligent predictive networks for analysis of chaos in stochastic differential SIS epidemic model with vaccination impact," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 251-283.

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