Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Machine Learning Regression Models to Predict Particulate Matter (PM2.5)

Authors
Koogan A. L. Letchumanan1, Naveen Palanichamy1, *
1Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
*Corresponding author. Email: p.naveen@mmu.edu.my
Corresponding Author
Naveen Palanichamy
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-094-7_36How to use a DOI?
Keywords
Air pollution; Smart cities; PM2.5; Decision tree; SVR; Multiple linear regression
Abstract

An increase in the quantity of fine particulates (PM2.5) in the air is a risk to the nation’s people since it can create uncontrolled repercussions such as the aggravation of cardiovascular disease and asthma. The issue of air pollution has lately surfaced as a critical concern in smart cities. The systematic technique of estimate particulate matter 2.5 using Machine Learning (ML) has received a lot of attention over the years. The main motive of the research is to employ machine learning models to find the best predicting model to forecast particulate matter PM2.5 in air quality in smart urban. Support Vector Regression, Decision Tree and Multiple Linear regression are chosen to study the application of machine learning in this research. The outcome of the prediction from respective machine learning then will be evaluated by the performance metrics to measure performance of the models. The outcome demonstrates that Decision Tree Regression is the best fit model for our present study.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
978-94-6463-094-7
ISSN
2589-4900
DOI
10.2991/978-94-6463-094-7_36How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Koogan A. L. Letchumanan
AU  - Naveen Palanichamy
PY  - 2022
DA  - 2022/12/27
TI  - Machine Learning Regression Models to Predict Particulate Matter (PM₂.₅)
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 458
EP  - 468
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_36
DO  - 10.2991/978-94-6463-094-7_36
ID  - Letchumanan2022
ER  -