Prediction of Ground-Level Ozone in Bengaluru Using Machine Learning Techniques

An increase in surface-level ozone concentration is a spiking issue in today’s world. Surface ozone is a secondary polluting agent that forms a mix of primary pollutants, meteorological variables, and volatile organic compounds. People are getting affected due to this pollutant because of its discreet nature. This paper focuses on trend analysis of the precursors with weather parameters and predicting the surface-level ozone concentration for the future. Data has been obtained from the public website Central Pollution Control Board (CPCB) for two stations Silk Board and Jayanagara 5
^th
Block in Bengaluru for study. The dataset has been split into parts to train and test the models. Extreme Gradient Boost provided the best R2 value with an accuracy of 95.7 percent.