Comparative Study on Air Pollution Prediction using Machine Learning Techniques

Abstract

The Air Quality Index (AQI) measures how air pollution affects human health. As pollution levels rise in Indian cities, we need reliable prediction models for better environmental management. This paper analyses different machine learning techniques for predicting AQI. We use Support Vector Regression (SVR), Random Forest Regression (RFR), and CatBoost Regression (CR) on data from New Delhi, Bangalore, Kolkata, and Hyderabad. We evaluate model performance using Root Mean Square Error (RMSE) and accuracy. Experimental results show that RFR performs best in most cities, while CR is most effective in New Delhi. To tackle dataset imbalance, we use the Synthetic Minority Oversampling Technique (SMOTE), which improves prediction accuracy for all models. Additionally, we assess other models, including SARIMA, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) for Ahmedabad. Among these, SVM with a radial basis function (RBF) kernel shows the best results. The findings emphasize how combining data balancing methods with machine learning models can improve AQI prediction. This approach can help with pollution control strategies and better decision-making.