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.