Abstract
Pakistan is one of the major coal producing country and also have the most serious coal mine accidents around the world. The proposed study performs experiments on a dataset contain reviews based on accident reasons occurred during mining work to reduce the accident rate. The aim of this study achieved by categorizing the reasons into different classes on the behalf of human behavior, roof dropping, and smoke inhalation. Then perform preprocessing on the reviews to clean the data. After preprocessing, the bag-of-words and TF-IDF are used singly and in combination to preserve meaningful information in extracted feature form. Finally, the Random Forest, Naive Bayes classifier, SVM, Decision Tree, Logistic Regression and proposed ensemble LSD (LR+SVM+DT) models are used to classify the accident reasons to analyze the most occurring once. The performance of the proposed approach is evaluated using average accuracy, precision, recall, and f1 score. The experiments reveal that a combined feature improved the performance of machine learning models. Ensemble LSD outperforms the other models and achieves 94% true acceptance rate when combined with TF-IDF and Bag of words.
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Javaid, M.A. et al. (2022). LSD: Discrimination of Coal Mining Accident’s Causes Based on Ensemble Machine Learning. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_39
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