Using Machine Learning to Predict Air Quality Index in New Delhi. (arXiv:2112.05753v1 [cs.LG])

Air quality has a significant impact on human health. Degradation in air
quality leads to a wide range of health issues, especially in children. The
ability to predict air quality enables the government and other concerned
organizations to take necessary steps to shield the most vulnerable, from being
exposed to the air with hazardous quality. Traditional approaches to this task
have very limited success because of a lack of access of such methods to
sufficient longitudinal data. In this paper, we use a Support Vector Regression
(SVR) model to forecast the levels of various pollutants and the air quality
index, using archive pollution data made publicly available by Central
Pollution Control Board and the US Embassy in New Delhi. Among the tested
methods, a Radial Basis Function (RBF) kernel produced the best results with
SVR. According to our experiments, using the whole range of available variables
produced better results than using features selected by principal component
analysis. The model predicts levels of various pollutants, like, sulfur
dioxide, carbon monoxide, nitrogen dioxide, particulate matter 2.5, and
ground-level ozone, as well as the Air Quality Index (AQI), at an accuracy of
93.4 percent.



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