PREDICTION OF AIR POLLUTION USING MACHINE LEARNING

Abstract

Due 
to 
human 
activities, 
industrialization and urbanization air is 
getting 
polluted. 
The major air 
pollutants are CO, NO, C6H6,etc. The 
concentration of air pollutants in 
ambient air is governed by the 
meteorological parameters such as 
atmospheric wind speed, wind direction, 
relative 
humidity, and temperature. 
Earlier techniques such as Probability, 
Statistics etc. were used to predict the 
quality of air, but those methods are 
very complex to predict, the Machine 
Learning (ML) is the better approach to 
predict the air quality. With the need to 
predict 
air 
relative 
humidity 
by 
considering various parameters such as 
CO, 
Tin 
oxide, 
nonmetallic 
hydrocarbons, Benzene, Titanium, NO, 
Tungsten, Indium oxide, Temperature 
etc, approach uses Linear Regression 
(LR), Support Vector Machine (SVM), 
Decision Tree (DT), Random Forest 
Method (RF) to predict the Relative 
humidity of air and uses Root Mean 
Square Error to predict the accuracy.