Air Quality Prediction Using Machine Learning Algorithms for Environmental Monitoring and Public Safety

Abstract

This project focuses on developing an Air Quality Prediction System using Machine Learning techniques to analyze and forecast air pollution levels. With rapid industrialization and urbanization, air pollution has become a major environmental and public health concern. Harmful pollutants such as Sulphur Dioxide (SO₂), Nitrogen Dioxide (NO₂), Respirable Suspended Particulate Matter (RSPM), Suspended Particulate Matter (SPM), and PM2.5 significantly affect human health and environmental sustainability. Therefore, predicting air quality in advance is essential for effective environmental monitoring and public safety. The proposed system utilizes machine learning algorithms such as Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine to analyze historical air quality data and predict pollutant concentrations. KEYWORDS: Machine Learning, Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine