Classification of industrial accidents in the energy sector using machine learning models

This paper aims to develop a machine learning based predictive model for detecting fatal and non-fatal industrial accidents in the energy sector. Four machine learning models such as Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB) and Random Forest (RF) were evaluated to identify the most suitable model. In addition, three feature extraction models—Bag of Words (BoW), TF-IDF and Word2Vec—were applied with the machine learning models to obtain a powerful model for fatal and non-fatal accident classification. The most popular metrics—accuracy, precision, F1-score and recall—were employed to assess the models. Testing confirmed that the achievement level amounted to 97% success. The investigation establishes the capability of machine learning to support energy sector safety management operations. The research results can support accident prevention through risk factor identification and safety hazard awareness improvement while enabling quantitative fatal and non-fatal accident predictions for better safety management system implementation.