Predictive Analysis of Air Pollutants Using Machine Learning

Abstract

AbstractAir pollution is a major concern nowadays as it affects all living organisms. Air quality is dependent on the pollutants present in the air which include oxides, ozone, carbon monoxide, particulate matter etc. Air pollution is now acknowledged as a major public health problem, causing a growing number of health effects that have been extensively documented by the findings of numerous research conducted throughout the world. The air quality index allows us to rate different sites according to the amount of pollution they have, showing the more contaminated areas as well as the frequency of potential risks. The AQI aids in determining changes in air quality over time, allowing for prediction of pollution and it's mitigation. Prediction of air quality helps people, and organizations in planning; managing various activities. In case of poor air quality, people can take precautions and look into the methods to reduce its adverse effects of it. It helps in protecting public health by predicting air pollutants. This literature review focuses on the various techniques used by different researchers in the prediction of air quality and air pollutants using machine learning. Machine learning techniques have been applied to different areas with various pollutants, and the performance of various machine learning algorithms is compared using performance metrics.KeywordsAir pollutantsAir quality indexMachine learningSupport Vector Machine (SVM)Random Forest (RF)AccuracyConfusion matrix