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Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit

Author

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  • Sasikumar Gurumoorthy

    (Department of Computer Science and Engineering, J. J. College of Engineering and Technology, Trichy 620009, India)

  • Aruna Kumari Kokku

    (Department of Computer Science and Engineering, SRKR Engineering College, Chinaamiram, Bhimavaram 534204, India)

  • Przemysław Falkowski-Gilski

    (Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland)

  • Parameshachari Bidare Divakarachari

    (Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru 560064, India)

Abstract

In the present scenario, air quality prediction (AQP) is a complex task due to high variability, volatility, and dynamic nature in space and time of particulates and pollutants. Recently, several nations have had poor air quality due to the high emission of particulate matter (PM 2.5 ) that affects human health conditions, especially in urban areas. In this research, a new optimization-based regression model was implemented for effective forecasting of air pollution. Firstly, the input data were acquired from a real-time Beijing PM 2.5 dataset recorded from 1 January 2010 to 31 December 2014. Additionally, the newer real-time dataset was recorded from 2016 to 2022 for four Indian cities: Cochin, Hyderabad, Chennai, and Bangalore. Then, data normalization was accomplished using the Min-Max normalization technique, along with correlation analysis for selecting highly correlated variables (wind direction, temperature, dew point, wind speed, and historical PM 2.5 ). Next, the important features from the highly correlated variables were selected by implementing an optimization algorithm named reinforced swarm optimization (RSO). Further, the selected optimal features were given to the bi-directional gated recurrent unit (Bi-GRU) model for effective AQP. The extensive numerical analysis shows that the proposed model obtained a mean absolute error ( M A E ) of 9.11 and 0.19 and a mean square error ( M S E ) of 2.82 and 0.26 on the Beijing PM 2.5 dataset and a real-time dataset. On both datasets, the error rate of the proposed model was minimal compared to other regression models.

Suggested Citation

  • Sasikumar Gurumoorthy & Aruna Kumari Kokku & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari, 2023. "Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11454-:d:1201190
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    References listed on IDEAS

    as
    1. Chelladurai Aarthi & Varatharaj Jeya Ramya & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari, 2023. "Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    2. Nien-Che Yang & Danish Mehmood, 2022. "Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems," Mathematics, MDPI, vol. 10(1), pages 1-20, January.
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    4. Mauro Castelli & Fabiana Martins Clemente & Aleš Popovič & Sara Silva & Leonardo Vanneschi, 2020. "A Machine Learning Approach to Predict Air Quality in California," Complexity, Hindawi, vol. 2020, pages 1-23, August.
    5. Muhammad Muhitur Rahman & Md Shafiullah & Syed Masiur Rahman & Abu Nasser Khondaker & Abduljamiu Amao & Md. Hasan Zahir, 2020. "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future," Sustainability, MDPI, vol. 12(10), pages 1-33, May.
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