A Probabilistic Approach to Dynamic Risk Scenario Identification

Abstract

Situation awareness is crucial for decision makers during an emergency. An efficient knowledge management can enhance situation awareness by providing information about the most relevant factors of the situation. Scenario analysis, based on morphological analysis, represents a structured method that can support the identification of such factors. Various studies based on this method have already been presented in the literature, e.g. as a method to strategically enhance disaster preparedness. In this paper, we introduce an approach that allows us to analyze current information in order to dynamically identify an emerging risk scenario. First, morphological analysis is applied to construct a scenario space. Second, in order to quantify the relations between scenario-factors, a Bayesian network model is implemented. For identification of the scenario, current information about the scenario-factors are needed. Information can be gathered from different sources, e.g. sensors or observations by emergency personnel and processed in the Bayesian network model to calculate the posterior probabilities of the parameters in the model. We illustrate the approach for risk scenario identification by applying it to an example in the context of emergency management. To conclude, we discuss the benefits and limitations of this approach as a knowledge management tool for enhancing situation awareness.