Prediction of Air Quality in Industrial Area

Abstract

Air quality monitoring and prediction in many industrial and urban areas, it has become one of the most important activities. Owing to different types of pollution, air quality is heavily affected. With increasing air pollution, efficient air quality monitoring models is to be implemented; these models gather data on the concentration of air pollutants. In a proposed approach, to solve three problems- prediction, interpolation and feature analysis, previously these problems were solved using three different models but now in the proposed system can solve these three problems in one model i.e Air Pollutant Prediction. This approach relates to unlabeled spatiotemporal data to enhance interpolation efficiency and air quality prediction. Experiments to test the proposed solution based on the real-time data sources collected by the Karnataka State Pollution Control Board (KSPCB), India. The goal of this research paper is to explore various strategies based on machine learning techniques for monitoring and predicating the air quality.