Study of Air Quality Detection using Machine Learning Techniques

Abstract

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Air, an important natural resource, has been compromised in terms of quality by economic activities. Pollution is a severe problem in areas where population density is high such as metropolitan cities. Various sorts of emissions caused by people’s actions, like transportation, power, and fuel use, are affecting air quality. Considerable research has been dedicated to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. We forecast air quality by using machine learning to predict the air quality index of a given area. The air quality index is a dataset for a typical measure used to indicate the pollutant (SO2 NO2, RSPM, SPM, and more) levels over a period. The ML models like a Decision tree and Random Forest Classifier are implemented and compared to show better accuracy.