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A dynamic Bayesian network‐based emergency decision‐making framework highlighting emergency propagations: Illustrated using the Fukushima nuclear accidents and the Covid‐19 pandemic
Author(s) -
Cai Yinan,
Golay Michael W.
Publication year - 2023
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.13928
Subject(s) - risk analysis (engineering) , computer science , bayesian network , process (computing) , generality , emergency management , adaptability , nuclear power , nuclear power plant , operations research , work (physics) , management science , engineering , artificial intelligence , business , political science , biology , operating system , mechanical engineering , psychology , ecology , physics , nuclear physics , law , psychotherapist
When facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. However, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. In the work reported here, we develop a framework based upon dynamic Bayesian networks in order to simulate emergency scenarios and support corresponding decisions. In this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. We illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. By incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. Scenarios developed from two public emergencies: the 2011 Fukushima nuclear power plant accidents and the Covid‐19 pandemic, are utilized to illustrate the framework in this paper. Capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real‐world situations. Our analysis results reveal many similarities between these two seemingly distinct events. This indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. Valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically.