Prediction and Analysis of Air Particulate matter in Delhi

Abstract

Human health has become a serious concern due to the rise in air pollution. In order to make informed decisions on air pollution management, air pollution analysis and forecast are critical. Pollutants smaller than 2.5 micrometres (PM2.5) are the primary indicator of air quality in a region. In this study, we used a variety of machine learning methods to construct a comprehensive model for predicting Delhi's air quality. Air quality levels were predicted using the Historic meteorological data which covers seven meteorological factors including wind speed, wind direction, solar radiation, ambient temperature, relative humidity, and PM2.5. Models for predicting PM2.5 levels are examined, and the MLP is shown to be the most accurate.