Dynamic and Multidimensional Risk Assessment Methodology for Coal Mine Fire Prevention: An INK-FBSD Approach

Current coal mine fire risk assessments often rely on static models and isolated factors, failing to capture the complex, dynamic interactions that lead to fires. To address this gap, we propose a comprehensive framework—termed INK-FBSD—integrating Interpretive Structural Modeling (ISM), the NK model, fuzzy Bayesian network analysis, and System Dynamics (SD) simulation. Using ISM, we identified and hierarchically structured 31 risk factors across human, equipment, environment, management, and fire protection domains, revealing that a robust mine safety accountability system is a pivotal root factor. The NK model quantifies how accident likelihood escalates as more factors interact—for example, four-factor couplings (e.g., equipment–environment–management–fire protection) yield significantly higher risk indices (T ≈ 0.34) than two-factor scenarios. The fuzzy Bayesian analysis estimates an overall 46% probability of a fire accident under current conditions, and diagnostic inference pinpoints excessive coal dust accumulation and neglected fire prevention as top contributors when an incident occurs (posterior probabilities 83% and 78%, respectively). Finally, SD simulations show how key risk factors (such as equipment failure and maintenance delays) can rapidly elevate to severe risk levels within 9–15 months without intervention, underscoring the need for continuous monitoring and proactive control. In summary, the INK-FBSD approach provides a multidimensional understanding of coal mine fire mechanisms and delivers practical guidance for safety management by prioritizing critical risk factors, anticipating high-risk coupling pathways, and informing more effective fire prevention and emergency response strategies.