Air Pollution Prediction using Machine Learning

Abstract

Industrial pollution is one of the most serious problems faced today. Long-term exposure to air pollution causes severe health issues including respiratory and lung disorders. Presently laws regarding industrial pollution monitoring and control are not stringent enough. The working dataset includes parameters of air in terms of ambient air as well as of the stack emission. On this data, various Machine Learning (ML) algorithms were applied for prediction of emission rate, and comparative analysis is done. These algorithms were implemented using python and the mean square error of each of these was measured to check for accuracy. It was observed that among all classifiers, the Multi-layer perceptron model was seen to have the least error. The air dispersion models are then applied to the predicted emission rate to calculate the dispersion of pollutants from the source that is at the stack level.