Critical Review of Air Quality Prediction using Machine Learning Techniques

Abstract

Artificial intelligence (AI) is a technique in which computers are designed to do tasks just like humans, they are designed to think, walk, talk and do anything that a living thing can do. Machine Learning (ML) is a field of research devoted to understanding and ’learning’ building methods, that is, methods that improve data to improve the performance of a particular set of tasks. This study is concerned with combining data of pollutants, meteorological, and traffic data with statistical temporal-spatial feature engineering to provide multi-step-ahead air quality forecasts for 24 and 48 hours. It examines a multivariate time series approach to modeling and forecasting the pollution of PM2.5, PM10, and NO2 at three air quality stations in India. The data-driven approach is thus believed to be an excellent complement for the knowledge-driven model.