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

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Abstract

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.

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Availability of data and material

Data are collected from a mine and are available with the corresponding author. It will be shared if needed by the reviewers, but cannot be published.

Code availability

Full python code available with the corresponding author and can be shared if needed by the reviewers, but cannot be published.

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Authors and Affiliations

  1. Mining Engineering, NIT, Rourkela, 769008, India

    D. P. Tripathy (Professor)

  2. Mining Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, 533437, India

    Satyajeet Parida (Assistant Professor & HoD)

  3. Project and Mines Division, State Mining Corporation Limited, Samtse, Bhutan

    Leki Khandu

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  1. D. P. Tripathy
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  2. Satyajeet Parida
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  3. Leki Khandu
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Correspondence to Satyajeet Parida.

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Tripathy, D.P., Parida, S. & Khandu, L. Safety Risk Assessment and Risk Prediction in Underground Coal Mines Using Machine Learning Techniques. J. Inst. Eng. India Ser. D 102, 495–504 (2021). https://doi.org/10.1007/s40033-021-00290-1

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  • DOI: https://doi.org/10.1007/s40033-021-00290-1

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