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Analysis of Mining Lost Time Incident Duration Influencing Factors Through Machine Learning

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Abstract

Despite technological advancements and organizational adjustments, lost time accidents are major issues in occupational safety. However, there is very limited work that focuses on variables influencing days lost as a result of occupational accidents. In this study, decision tree and artificial neural network methods were used as machine learning techniques to investigate the impact of factors on accident lost day duration. Degree of injury, worker age, and worker activity were found to be the top three variables impacting loss of time from work. It was also identified that the mining method, location, and nature of injury had a moderate influence on duration lost due to occupational accidents. However, worker experience and ore type did not have any significant impact on the duration, which is an unexpected result. These results confirmed that some accident factors that might have a large influence on the number of mine accidents can be less critical when it comes to accident lost day duration.

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Authors and Affiliations

  1. Department of Mining Engineering, Faculty of Engineering and Natural Sciences, Gumushane University, Gumushane, Turkey

    Muhammet Mustafa Kahraman

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  1. Muhammet Mustafa Kahraman
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Correspondence to Muhammet Mustafa Kahraman.

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Kahraman, M.M. Analysis of Mining Lost Time Incident Duration Influencing Factors Through Machine Learning. Mining, Metallurgy & Exploration 38, 1031–1039 (2021). https://doi.org/10.1007/s42461-021-00396-w

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  • DOI: https://doi.org/10.1007/s42461-021-00396-w

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