A Bayesian Network-based Scenario-Response Model for Situation Deduction in Emergency Management of Rainstorm Scenarios

The prevalence of safety hazards has intensified, underscoring the pressing need for effective responses to sudden disasters and enhanced emergency management. The traditional forecast-response paradigm is no longer adequately addressing the management and decision-making needs for contemporary emergency incidents. Existing emergency management frameworks often struggle with the systemic characteristics of unexpected events. This study proposes a Bayesian network-based scenario-response framework for situation deduction in Emergency Management, taking the torrential rain disaster in Henan Province as a point of reference. In this context, the fundamental units of disaster-forming environment (E), emergency measures (M), and scenario state (S) are employed to construct a coherent and dynamic scenario evolution chain, which effectively depicts the progression of the scenario linkage process. This approach facilitates a deeper understanding of the evolution of emergencies and enhances predictive capacity.