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IJSTR >> Volume 9 - Issue 4, April 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Air Pollution Prediction Using Machine Learning Supervised Learning Approach

[Full Text]

 

AUTHOR(S)

Madhuri VM, Samyama Gunjal GH, Savitha Kamalapurkar

 

KEYWORDS

Air Pollution, Decision Tree, Linear Regression, Machine Learning, Random Forest, Supervised Learning, SVM.

 

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.

 

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