Podicheti Ravi Kiran - Department of Mining Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, India
Ramchandar Karra - Department of Mining Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, India

Opencast coal mines play a crucial role in meeting the energy demands of a country. However, the operations will result in deterioration of ambient air quality, particularly due to particulate emissions. The dispersion of particulate matter will vary based on the mining parameters and local meteorological conditions. There is a need to establish a suitable model for predicting the concentration of particulate matter on a regional basis. Though a number of dispersion models exist for prediction of dust concentration due to opencast mining, machine learning offers several advantages over traditional modeling techniques in terms of data driven insights, non-linearity, flexibility, handling complex interactions, anomaly detection, etc. An attempt has been made to assess the dispersion of particulate matter using machine learning techniques by considering the mining and meteorological parameters. Historical data comprising of mine working parameters, meteorological conditions, and particulate matter pertaining to one of the operating opencast coal mines in southern India has been utilized for the study. The data has been analyzed using different machine learning techniques like bagging, random forest, and decision tree. The performance metrics of test data are compared for different models in order to find the best fit model among the three techniques. It is found that for PM۱۰, many of the times bagging technique gave a better accuracy, and for PM۲.۵, decision tree technique gave a better accuracy. Integration of mine working parameters with meteorological conditions and historical data of particulate matter in developing the model using machine learning techniques has helped in making more accurate predictions.

opencast coal mine, PM۱۰, PM۲.۵, dispersion, Machine learning