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

Sasikumar Gurumoorthy, Aruna Kumari Kokku, Przemysław Falkowski-Gilski () and Parameshachari Bidare Divakarachari ()
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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

Sustainability, 2023, vol. 15, issue 14, 1-19

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.

Keywords: air quality prediction; bi-directional gated recurrent unit; correlation analysis; Min-Max normalization technique; reinforced swarm optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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