Air Quality Index Prediction using Machine Learning Algorithms

Abstract

The global environment is presently facing a key issue of air pollution. The four air pollutants which are becoming a concerning intimidation to human health are respirble particulate matter, nitrogen oxide, particle matter, and sulfur dioxide. A vast amount of air quality data is collected in different monitoring stations throughout the world. The collected data can be analyzed to forecast the air quality index (AQI) of future. This paper proposes machine learning algorithms such as random forest, support vector machine, self adaptive resource allocation to predict the future AQI. Tamil Nadu Pollution Control Board (TNPCN) deployed air pollution monitoring station in five regions. Air pollutant of PM10, PM2.5, SO2 and NO2 are monitord and AQI is calculated.. The data collected from January 2019 to November 2019 by TNPCN and also AQI of previous five years were used This system attempts to predict the level of pollutant PM,SO2,NO2 in the air to detect the AQI.