Machine Learning-Based A Comparative Analysis for Air Quality Prediction

Abstract

Air pollution affects human life negatively, especially in terms of health, and causes the death of millions of people every year. Today, air pollution in many regions is still above the limits indicated by the World Health Organization. In this study, the prediction of the rate of PM2.5, which is an important air pollutant, in the Beijing region of China is emphasized. For this purpose, weather prediction models were created using Random Forest Algorithm, Support Vector Regression, XGBoost and K-Nearest Neighbor Algorithm, which are popular machine learning algorithms, and the results were compared using various metrics. The best prediction result in all the metrics used was obtained with the Support Vector Regression method.