Injury Prediction for Canadian Mineral Exploration Using Machine Learning

The mineral exploration industry is a vital contributor to the Canadian economy, yet it remains among the most hazardous sectors due to the complex and risky nature of mining operations. The primary objective of this research is to comprehend and predict the specific nature of injury severity within Canada's mineral exploration field to enhance existing occupational health and safety measures. The proposed research is distinctive as it utilizes data from the entire mineral exploration industry in Canada, gathered by the Prospectors and Developers Association of Canada (PDAC). Advanced machine learning (ML) techniques are employed to construct a framework capable of predicting and monitoring injuries in four different classes. Following a description of the dataset's distribution, eight distinct machine learning methods, including Support Vector Machine, Convolutional Neural Network, Bayesian Neural Network (BNN), logistic regression, decision trees, random forest, Gradient Boosting, and Long Short-Term Memory (RNN LSTM), were applied to predict the nature of injury in different mining activities. The results of the framework indicated that multi-classification with RNN-LSTM outperformed other algorithms, accurately identifying the degree of injury with 97% accuracy across all metrics. These findings have the potential to significantly contribute to injury prevention efforts by increasing awareness of potential safety risks and providing quantitative predictions of fatal injuries and future accidents in mining exploration fields.