A Bayesian Network Model for Rough Estimations of Casualties by Strong Earthquakes in Emergency Mode


 Rough estimations in emergency mode are now playing an important role in making key decisions for managing disasters including search and rescue. Most of the studies only paid attention to the earthquakes and ignored the presence of disaster chains and the hazard interactions in earthquakes. Bayesian Networks are ideal tools to explore the causal relationships between events, combine prior knowledge and observed data, and are integrated to solve uncertain problems. In such situations, we present improvements based on a Bayesian Network Model in approaches to estimations of casualties in earthquakes. According to the development of the earthquake disaster chain in literature, the proposed model extracts the key events of earthquakes, considers the hazard interactions, and constructs the Bayesian Networks based on a scenario-based method, to deal with the events in the earthquakes. In the model, lifeline system damages, fires, landslides, and debris flow have been integrated into the networks. The conditional probability tables are encoded by using the collected cases. Validations in the Netica allow the simulation of expected shaking intensity and estimation of the expected casualties by strong earthquakes in emergency mode. Compared to the literature, the method is closer to the fact in the rough estimations, providing important information for our response to earthquakes. Further, rough estimations are started when only seismic intensity or fewer earthquake source parameters are available.
 
