Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques

Abstract

Air pollution is a major environmental issue and machine learning techniques play an important role in analyzing and forecasting these data sets. Air quality is an outcome of the complex interaction of several factors involving the chemical reactions, meteorological parameters, and emissions from natural and anthropogenic sources. In this paper, we propose an efficient combined technique that takes the benefits of statistical techniques and machine learning techniques to predict/forecast the Air Quality and Pollution in particular regions. This work also indicates that prediction performance varies over different regions/cities in India. We used time series analysis, regression and Ada-boosting to anticipate PM 2.5 concentration levels in several locations throughout Hyderabad on an annual basis, depending on numerous atmospheric and surface parameters like wind speed, air temperature, pressure, and so on. Dataset for this investigation is taken from Kaggle and experimented with proposed method and comparison results of our experiments are then plotted.