Scenario deduction of explosion accident based on fuzzy dynamic Bayesian network

Abstract

In order to effectively tackle dynamic and uncertain challenges related to hazardous chemical accidents' occurrence and progression, this research establishes a model for identifying scenario elements in hazardous chemical explosion incidents based on crucial scenario states, vulnerable environmental conditions, emergency responses, and evolving objectives during these incidents. Subsequently applying this model to dynamic Bayesian network (DBN) modeling enables integration of triangular fuzzy set theory into DBN methodology for constructing a fuzzy dynamic Bayesian network (FDBN) specific to hazardous chemical accidents. Additionally leveraging complex networking expertise allows conducting sensitivity analyses along with critical node assessments pertaining to impact factor nodes associated with disaster-prone environments as well as emergency responses. The findings demonstrate that computed probabilities within this simulated scenario network align with actual occurrences of these incidents while also simulating their evolutionary trajectories across diverse disaster-prone settings alongside various emergency response scenarios. Moreover, this investigation identifies pivotal influencing factors including ambient surroundings as well as firefighting capabilities thereby furnishing essential decision support for managing such emergencies.