A Data-Driven Approach to Air Quality Prediction in Jharkhands Coalfields using Machine Learning

Air pollution is a considerable issue to human health and requires prompt reduction in anthropogenic zones like industrial plants. Every day, harmful pollutants including CO, CO2, particulate matter, NO2, SO2, and NH3 are discharged into our environment. The mining industry stands out as a key source of air pollution, particularly due to the substantial generation of Particulate Matter. This leads to an unhealthy atmosphere for both mine workers and neighboring communities. This study focuses on monitoring air quality in Jharkhand, known as India's largest coal-producing state in India. The state hosts 38 opencast coal mines and 5 underground coal mines. Accurate prediction of air quality is crucial. Traditional measurement methods often yield imprecise results and require complex mathematical computations. A variety of supervised algorithms used for prediction logistic regression with 0.7482, decision tree classifier with 0.9989, random forest with 0.9989, and KNN with 0.9898 accuracy.