Air Quality Analysis of Madurai City using Machine learning approach
Author(s):
MUGESH S, Gayathri M
Keywords:
PM2.5, PM10, Pollutants, Analysis, AQI, Regression
Abstract
The deteriorating air quality in urban centres like Madurai necessitates a comprehensive understanding of its contributing factors and potential mitigation strategies. This study presents an in-depth analysis and modelling of air quality parameters in Madurai, focusing on particulate matter (PM2.5 and PM10). Utilizing a combination of ground-based monitoring data, satellite imagery, and meteorological observations, this research investigates the spatial and temporal variations of air pollutants across different regions of Madurai. Statistical analyses and machine learning techniques are employed to identify key sources and factors influencing air pollution levels. Several types of regression analysis such as Linear regression, Random Forest regression, Gradient boosting regression, ridge regression, lasso regression, MLP regression, K Neighbours (KNN) regression, and Decision tree regression are performed and the analysis best suited for predicting the air quality of Madurai is depicted. Additionally, predictive models are developed to forecast future air quality scenarios under varying socio-economic and environmental conditions. The findings of this study contribute valuable insights for policymakers, urban planners, and environmental agencies to formulate effective strategies for improving air quality and public health in Madurai.
Article Details
Unique Paper ID: 167078

Publication Volume & Issue: Volume 11, Issue 3

Page(s): 100 - 107
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