Skip to main content

Prediction and Analysis of Air Pollution Using Machine Learning

Abstract

Air pollution is one of the most dangerous forms of pollution. We aim to find the pollution prediction and the air quality in Kerala, India. Through this paper, machine learning algorithms such as ridge regression, linear regression, random forest regression, LASSO regression, and elastic net regression are opted for analyzing and predicting air quality. This analysis helps to dissect how it will be beneficial to industrial sectors in the conversion of pollutants into a useful by-product.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. Haiping Xu, Rebollar D, He H, Chong L, Liu Y, Liu C, Sun C-J, Li T, Muntean JV, Winans RE, Liu D-J. Highly selective electrocatalytic CO2 reduction to ethanol by metallic clusters dynamically formed from atomically dispersed copper. Nat Energy. 2020. https://doi.org/10.1038/s41560-020-0666-x.

    Article  Google Scholar 

  2. Shreyas S, Varsha T, Rohit M, Vranda A, Swizel M, Vaylon F, Vassant S. Air pollution prediction using machine learning, IEEE Bombay Section Signature Conference (IBSSC) 2022;https://doi.org/10.1109/IBSSC51096.2020.9332184

  3. Vineeta AB, Asha SM, Pranay M. Machine learning based prediction system for detecting air pollution. Int J Eng Res Technol. 2019;8:9.

    Google Scholar 

  4. Madhuri VM, Samyama GGH, Savitha K. Air pollution prediction using machine learning supervised learning approach. Int J Sci Technol Res. 2020;9:4.

    Google Scholar 

  5. Venkat RP, Uhasri PK, Srikanth HKR. Air quality prediction of data log by machine learning. 2020 6th International Conference on Advanced Computing & Communication Systems (ICACCS)

  6. Clara MN, Uwe P, Ivan T, Birger B (2021) New photolytic converter for improving aircraft measurements of NO2 via chemiluminescence. doi: 10.5194/amt-2021-180

  7. https://drive.google.com/file/d/1_BYoPZuViDmA2ipfNkn97UPd17qIRsuK/view?usp=sharing

  8. https://drive.google.com/file/d/1cVZ5R2RC3xnKankZSaNqjSa9C7xnP8lC/view?usp=sharing

  9. https://docs.google.com/spreadsheets/d/1EylER2ZoVB1lqbbSMvPEyuxHOvdMVnfQ/edit?usp=sharing&ouid=112297395183142401916&rtpof=true&sd=true

Download references

Acknowledgements

We, Megha Satish and Manju Murali, are grateful to the Kerala State Pollution Control Board Ernakulam for providing the data.

Funding

The authors declared that no funding was received for this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manju Murali.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Intelligent Systems guest edited by Geetha Ganesan, Lalit Garg, Renu Dhir, Vijay Kumar and Manik Sharma”.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Murali, M., Satish, M. & Rajalakshmi, V.R. Prediction and Analysis of Air Pollution Using Machine Learning. SN COMPUT. SCI. 3, 483 (2022). https://doi.org/10.1007/s42979-022-01383-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01383-8

Keywords

  • Ridge regression
  • Linear regression
  • Random forest regression
  • LASSO regression
  • Elastic net regression