Discovering the underground coal mining accident patterns in Spain from 2003 to 2021: Insights through machine learning techniques

Abstract

The safety of underground coal mining has always been a global concern, involving the stable supply of energy and stakes in miners’ lives. Lessons learned from historical accidents and transforming into practical experience help reduce the quantity and severity of accidents. In this study, six ensemble learning techniques, including AdaBoost, Extra Trees, GBDT, LightGBM, Random Forest, and XGBoost, were used to investigate the correlation between accident-causing factors and severity. Firstly, 39 487 underground coal mine accidents data was obtained from Spain, variables were categorized and coded. To address the extreme class imbalance, a new dataset (2468 cases) was obtained by data sampling from the original database. Subsequently, the new dataset was randomly divided into training sets (75% of the data) and test sets (25% of the data), then the hyperparameters of each model were optimized and configured. Thirdly, the models’ performance was evaluated on the test data by five metrics (accuracy, Cohen’s Kappa, precision, recall, and F 1 ). Finally, accident patterns were derived from the identified variables along with preventive strategies. Results show that tree-based ensemble learning model performs better compared to the boosting model, and the relative importance of seven variables were determined, where previous cause (PC) and material agent (MA) are the most important factors, followed by the miner’s physical activity (PA), age (A), and experience (E), scale (S) and preventive organization (PO) are in the third tier. Furthermore, the type of accident and injury caused by PC were confirmed. Working with hand tools, younger age, lack of experience, small-scale coal mines, and unfit preventive organization increased the risk of accidents. This study not only facilitates the prediction of accident severity but also provides strategies for preventing and mitigating accidents. • This study reveals the trend of underground coal mine accidents in Spain, utilizing data sampling techniques to address the extreme class imbalance, and five model evaluation metrics are adopted to evaluate models’ performance. • Utilizing ensemble learning techniques, it uncovers the intricate correlations between accident-causing factors and severity, determines the relative importance of input variables, and sheds light on factors influencing accident severity. • Through meticulous analysis, unearthing accident patterns and providing reasonable strategies. The findings have significant implications for safety management and policy development.

Topics & Concepts