Application and Comparison of Machine Learning and Traditional Regression Models for Air Quality Index Prediction in India

Yuchen Liu

doi:10.54254/2753-8818/2025.22547

Research Article

Open access

Published on 6 May 2025

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Liu,Y. (2025). Application and Comparison of Machine Learning and Traditional Regression Models for Air Quality Index Prediction in India. Theoretical and Natural Science,105,15-21.

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Application and Comparison of Machine Learning and Traditional Regression Models for Air Quality Index Prediction in India

Yuchen Liu *^,1,

¹ School of Mathematics, China University of Mining and Technology, Xuzhou, Jiangsu, China, 221116

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2025.22547

Abstract

Air pollution, a global environmental issue, is a growing concern in developing countries, particularly India. This study analyzes air quality data from 10 major districts of India from 2020-2024, focusing on the impact of seven pollutant indicators on the Air Quality Index (AQI). Data normalization was used to calculate AQI values based on international standards. Three linear regression models were constructed: a full parameter model, one focusing only on particulate matter (PM2.5, PM10), and one excluding other indicators. The experimental results show that the model with particulate matter as a predictor variable outperforms other models, confirming that PM2.5 and PM10 are key indicators for AQI prediction in Indian regions.

Keywords

Air Quality Index, Linear Regression, Machine Learning, Support Vector Machines, Neural Networks

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References

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Cite this article

Liu,Y. (2025). Application and Comparison of Machine Learning and Traditional Regression Models for Air Quality Index Prediction in India. Theoretical and Natural Science,105,15-21.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation

Conference website: https://2025.confmpcs.org/

ISBN：978-1-80590-077-1(Print) / 978-1-80590-078-8(Online)

Conference date: 27 June 2025

Editor：Anil Fernando

Series: Theoretical and Natural Science

Volume number: Vol.105

ISSN：2753-8818(Print) / 2753-8826(Online)

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