Abstract
Labor safety at workplaces is a critical human rights concern in all industries around the world. Coal mines are considered one of the most dangerous workplaces and every year thousands of miners around the world die or get severe injuries in mining accidents. To make efficient technology-based accident mitigation plans for such work environments, the analysis of reasons which cause these accidents is of great value. This study contributes to the coal mines domain and proposed an approach using machine learning techniques to identify the reasons for the accidents. In our approach, a dataset containing the causes for accidents in text form that occurred in the past in coal mines has been used. We performed preprocessing to clean text data and then extract features to train the machine learning model using the term frequency-inverse document frequency (TF-IDF) technique. This study proposed the voting-based hybrid classifier (VHC) which is a combination of three individual machine learning models random forest, support vector classifier, and logistic regression using soft voting criteria. Evaluation of the model has been done in terms of accuracy, precision, recall, and f1 score. VHC outperforms all other stat of the art models by achieving the highest 0.96 accuracy score.
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Data Availibility
The used dataset in experiments is publicly available at Kaggle on the given linkhttps://www. kaggle.com/furqanrustam118/coal-minin-datase.
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Acknowledgements
This research was supported by the Florida Center for Advanced Analytics and Data Science funded by Ernesto.Net (under the Algorithms for Good Grant)
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Javaid, A., Siddique, M.A., Reshi, A.A. et al. Coal mining accident causes classification using voting-based hybrid classifier (VHC). J Ambient Intell Human Comput 14, 13211–13221 (2023). https://doi.org/10.1007/s12652-022-03779-z
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DOI: https://doi.org/10.1007/s12652-022-03779-z