Research On Emergent Events Decision Making Information Analysis Method Based On Bayesian Network

Emergent events are bursty, complex, extremly uncertain, and widely involved,which makes it a highly complicated problem and needs the decision makingprocess to be done within a short time with fewer pre-arranged plannings.Obviously, the traditional decision-making methods with pre-arranged plannings cannot well meet the needs of the emergency management.Therefore, how to fast andeffectively analyse the data and evolution laws of emergent events in order tosupport the decision making with referential information has become a key issue inthe field of emergent events decision-making and management.Under the circumstance of the industrial informationization, emergent eventsdecision-making support methods with artificial intelligence technology can forecastand assess the emergency tendency based on effectively analysis of emergent eventsdata, which shows a new way to support emergent events decision making. As animportant branch of artificial intelligence research, Bayesian networks is combinedwith the knowledge of graph theory and statistics, and can manage uncertaintyproblems, express and fuse multi-source information. Therefore, applying Bayesiannetworks into emergent events decision making, researching the emergent eventsdecision-making information analysis method based on Bayesian networks hasnotable theoretical and practical significance.The domestic and foreign research achievements are analyzed and summarized.Then, based on emergency management theory, system dynamics theory, Bayesiannetwork theory, case reasoning theory and accordingly techniques, the emergentevents decision-making information analysis method based on Bayesian networks isdeeply researched by qualitative and quantitative analysis.First, taking the system structure of emergent events evolution as the researchobject, the Bayesian network topology learning method for supporting emergentdecision making of emergent events is studied. In order to adapt to incompleteinformation, uncertain development and massive data, based on model average, animproved Bayesian network topology learning method which combined bothadvantages of K2and MCMC is proposed. A Bayesian Network topologyoptimization method is also proposed to improve the system efficiency.Second, based on system dynamics and Bayesian network theories, theemergency assessment method based on Bayesian network is studied. By analyzingthe key parameters of emergent events evolution, the causal feedbacks of keyparameters within different subsystems are obtained. And then, based on maximumlikelihood estimate, maximum a posteriori estimation and expectation maximization algorithm, an improved Bayesian network parameter learning method is proposed inorder to adapt to the changing modeling circumstance and the need of real-timeupdate ability. An example application to a power network disaster assessment ispresented to illustrate and test the proposed method.Third, in order to adapt to the complexity and time sensitive of the emergentevents decision making, a case adaptation method based on Bayesian network foremergent events decision making is proposed. The historical Bayesian networkmodels are stored in a case database, a new Bayesian network model can beobtained by case retrieve and case revision, which can enhance the reuse ofhistorical models. The proposed method do not have a huge search space or needsample data. Compared with the traditional Bayesian network modeling method, thismethod is more effcient and can be an important assistant to the Bayesian networkmodeling method for emergent events decision-making support model.Finally, an application of Bayesian network for emergency decision making inthe flood disaster is illustrated. With the analysis of the flood evolution andemergency decision making for flood, the Bayesian network models are built basedon the proposed Bayesian network method for emergent events decision makinginformation analysis, which shows the ability to support the flood disasteremergency decision making with referential information. Therefore, the proposedemergent events decision making information analysis method based on Bayesiannetwork is verified.