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Comparative Analysis of Machine Learning Techniques in Air Quality Index (AQI) prediction in smart cities

Author

Listed:
  • Gaurav Sharma

    (Seth Jai Parkash Mukand Lal Institute of Engineering and Technology)

  • Savita Khurana

    (Seth Jai Parkash Mukand Lal Institute of Engineering and Technology)

  • Nitin Saina

    (Seth Jai Parkash Mukand Lal Institute of Engineering and Technology)

  • Shivansh

    (Seth Jai Parkash Mukand Lal Institute of Engineering and Technology)

  • Garima Gupta

    (Seth Jai Parkash Mukand Lal Institute of Engineering and Technology)

Abstract

Air pollution is now one of the world's most serious environmental problems. It represents a significant hazard to both health and the climate. Urban air quality is steadily declining, affecting not only the air itself but also impacting the quality of water and land. This paper explores the utilization of machine learning-based algorithms for analysis and prediction of air quality in smart cities. In this paper, smart cities for which air quality index (AQI) is calculated are Ahmedabad, Delhi, Lucknow, Gurugram, and Mumbai. The comparative analysis of different Machine Learning algorithms such as Random Forest Regression (RF), Decision Tree Regression, Linear regression, XgBoost and proposed hybrid model which is combination of Random forest and Xgboost model, have been discussed in the paper. The analysis has been carried out using a machine learning-based algorithm to determine which pollutant is the primary source of pollution in a smart city so that preventative steps can be implemented to reduce air pollution.

Suggested Citation

  • Gaurav Sharma & Savita Khurana & Nitin Saina & Shivansh & Garima Gupta, 2024. "Comparative Analysis of Machine Learning Techniques in Air Quality Index (AQI) prediction in smart cities," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3060-3075, July.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02315-w
    DOI: 10.1007/s13198-024-02315-w
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