Revolutionizing Risk Management: A Markov-Bayesian Fusion for Accident Prediction and Prevention

This study introduces a Hybrid Markov–Bayesian Framework for predicting and managing accident risks in high-risk industries, with a specific focus on the mining sector. The framework integrates Markov models to analyze dynamic risk transitions and Bayesian networks to infer causal relationships among key human and environmental factors. Drawing from a comprehensive dataset of mining operations, the framework evaluates variables such as age, experience, task type, and injury characteristics to predict and control accident risks. The results highlight the model's high performance, achieving an accuracy of ۸۷%, precision of ۸۵%, and an F۱-score of ۰.۸۴. This innovative approach enables real-time safety interventions and proactive risk management strategies. The findings underscore the framework's potential to improve workplace safety and serve as a scalable tool for accident prevention in other high-risk industries. Future research will focus on enhancing the framework’s adaptability and incorporating additional contextual variables for broader applicability.

Keywords:

Hybrid Framework , Markov model , Bayesian Network , Accident Risk , Mining industry