Air Quality Prediction Using Classification Techniques

Abstract

           In several industry applications and metro areas nowadays, air internal control and restoration is one of the most significant practices. The quality of the air is harmed by various types of pollution caused by transportation, power, and petroleum usage, among other things. The evidence of noxious particles is posing significant consequence to the improving the overall quality of life in green infrastructure. It is important to integrate reliable environmental monitoring models that collect data on pollutant levels and provide an analysis of environmental pollution in each area compared to air pollution. Air pollution harms both the atmosphere and human health, resulting in global warming, acid rain, heart disease, lung cancer, and skin cancer among the population. More analysis is needed to assess raw information in view of identify pollutants. ML algorithms and deep learning ensure that potential AQI can be predicted and effective steps can be taken. As a result, air quality estimation and prediction has become a major research subject. We will look at various ML [26] and DL algorithms from which can identify air quality in this analysis.