Modeling method of cascading crisis events based on merging Bayesian Network

a r t i c l e i n f o This paper presents a Bayesian Network (BN)-based modeling method for cascading crisis events. Crisis events have occurred more frequently in recent years, such as typhoons, rainstorms, and floods, posing a great threat to humans. Addressing these crises requires a more effective method for crisis early-warning and disaster mitigation in crisis management. However, few modeling methods can combine the crisis chain reaction (macro-view) and the elements within the crisis event (micro-view) in a cascading crisis events. Existing classical methods fail to consider the causal relations linking the micro to macro level in crisis events, which affects the forecasting accuracy and effectiveness. Based on systems theory, this paper first abstracts the crisis event as a three-layer structure model consisting of input elements, state elements and output elements from a micro-view. Next, a cascading crisis events Bayesian Network (CCEBN) model is developed by merging the single crisis events Bayesian Networks (SCEBNs). This method efficiently combines the crisis event's micro-view and the macro-view. The proposed BN-based model makes it possible to forecast and analyze the chain reaction path and the potential losses due to a crisis event. Finally, sample application is provided to illustrate the utility of the model. The experimental results indicate that the method can effectively improve the forecasting accuracy. A well-performed emergency decision support system (EDSS) plays a crucial role in emergency-warning and disaster mitigation of crisis management. To save lives and prevent additional property damage, several models for EDSS have been developed in the field of crisis management. However, these models did not comprehensively utilize the information of the whole crisis environment, which was restricted to be specific to its own context. Most of the former studies focused only on a single crisis event (such as the artificial neural network-based EDSS [1], nuclear emergencies [2], hurricanes in Florida [3], floods in Italy [4], among others). These methods overlook the interactions among different crisis events, which results in a low forecasting accuracy. When a crisis event occurs, it usually leads to secondary events or derived events. Specifically, there are causal relations among the different crisis events. The occurrence and development of one event always has impact on the other events. This phenomenon can be called chain reaction of crisis events. These corresponding associated crisis events form a cascading crisis event. It is helpful to improve the effectiveness of crisis management …