Safety Risk Assessment and Risk Prediction in Underground Coal Mines Using Machine Learning Techniques

Risk management focusses on the identification of uncertainties and its impacts associated with various functional activities carried out to achieve various mandates, goals and objectives of the company. To assess the risk level (as ‘very high’, ‘high’, ‘medium’ or ‘low’), consequences and likelihoo...

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Bibliographic Details
Published in:Journal of the Institution of Engineers (India): Series D 2021-12, Vol.102 (2), p.495-504
Main Authors: Tripathy, D. P., Parida, Satyajeet, Khandu, Leki
Format: Article
Language:English
Subjects:
Classification
Coal mines
Decision trees
Engineering
Machine learning
Original Contribution
Risk assessment
Risk levels
Risk management
Underground mines
Underground mining
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Summary:Risk management focusses on the identification of uncertainties and its impacts associated with various functional activities carried out to achieve various mandates, goals and objectives of the company. To assess the risk level (as ‘very high’, ‘high’, ‘medium’ or ‘low’), consequences and likelihood analysis are to be done based on the judgmental knowledge and experiences of the participants. The traditional methods of risk classification are time consuming and laborious if the inputs are voluminous. In this study, the hazards occurring in different sections of underground mining have been categorized, and associated risks have been predicted using different machine learning modelling techniques, namely KNN, SVM, logistic regression and decision tree by keeping the prescribed guidelines of the DGMS intact and using it as the basic building blocks to model the machine learning classification models.
ISSN:2250-2122
2250-2130
DOI:10.1007/s40033-021-00290-1