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

Gaurav Sharma (), Savita Khurana (), Nitin Saina (), Shivansh () and Garima Gupta ()
Additional contact information
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

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 17, 3060-3075

Abstract: 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.

Keywords: AQI; Air pollution; Machine learning; Regression; Pollutant (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02315-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02315-w

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02315-w

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().