Air Quality Index Detection Using Random Forest Algorithm

EasyChair Preprint 9994

Abstract

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 meteorology data. In this project, Random Forest model is used to forecast the levels of various pollutants. 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.

Keyphrases: Meteorology Data, Random Forest Model, particulate matter

@booklet{EasyChair:9994,
  author    = {A.Peter Soosai Anandaraj and Hari Krishnam Raju Keertipati and Adithya Gunda},
  title     = {Air Quality Index Detection Using Random Forest Algorithm},
  howpublished = {EasyChair Preprint 9994},
  year      = {EasyChair, 2023}}