A Bayesian-based Decision Support Tool for assessing and managing rock fall disasters

Recent catastrophic events demonstrate the importance of the post-disaster interventions, where the management of rescue operations and recovery assistance to citizens, the coordination of several organizations and the allocation of proper resources are truly critical issues. Many Civil Defence Systems have developed the knowledge and the experience to understand in “real time” the actual situation of the disaster context (e.g. through GPS tools for continuous monitoring or using ICT infrastructures to communicate with field forces); nevertheless, due to the complexity of the situation and the amount of information to process, specific decision support tools are needed that allow the decision maker to provide prompt responses and effective coordination of all the actors of the Civil Defence System. The paper describes a decision support tool, based on Bayesian Belief Network (BBN), for rescue and recovery operations during a rock fall disaster; the variables in the model allows both the representation of the causal chain of physical phenomenon and the assessment of other context factors affecting the emergency planning and management. The quantification of this advanced Decision Support System (DSS) is based on data gathered from available monitoring system and on experts’ judgements. The paper starts describing the Bayesian-based approach, showing the ability of the model to integrate different data-sets in different steps of the analysis (from the preliminary definition of the hydro-geological phenomenon to the integration of expert’s observation into the model). Finally, a brief case study is presented, referring to a rock fall event in an urban area, where the capability of the BBN to incorporate direct observations and describe a real case of rock fall is shown.