Toward Temporal Multi-Hazard Risk Assessment Using Dynamic Bayesian Network Analysis

This study addresses the critical need for dynamic multi-hazard risk assessment in interconnected infrastructure systems through the integration of the Bayesian Network and Strongest Path Method (BN-SPM). By incorporating Dynamic Bayesian Networks (DBNs), this study enhances the temporal dimension of risk assessment, enabling an in-depth analysis of real-time disruptions and restorations, thus advancing beyond the static capabilities of BN-SPM. Using a case study of Saint Lucia, a region susceptible to diverse hazards, such as flooding, the research investigates critical infrastructure networks including the Hewannora International Airport (HIA), and tourism-related component. This integration also allows for the comprehensive modeling of probabilistic conditions and functional dynamics of disruptions and restoration, capturing the cascading effects and temporal behavior of multi-hazard scenarios. By focusing on various failure scenarios within interconnected infrastructure systems, including the airport and tourism sectors, we provide detailed insights into the dynamic vulnerabilities. This study represents a paradigm shift in risk assessment methodologies, bridging the gap between static evaluations and the dynamic nature of contemporary challenges. This research makes a significant contribution to the field by providing a robust framework for addressing the complexities of interconnected infrastructure systems in the face of dynamic impacts from multi-hazard scenarios. The findings offer guidance for the development of more effective multi-hazard risk mitigation strategies. The enhanced understanding gained from this study empowers stakeholders to proactively navigate the evolving landscape of risks, paving the way for resilient infrastructure development and sustainable risk management practices in the modern era.