CAESAR II Tool: Complementary Analyses for Emergency Planning Based on Seismic Risks Impact Evaluations

Italy is a country with high seismic hazard, however since the delay in the seismic classification of the national territory, most of the existing building heritage does not comply with the current technical standards for buildings. The seismic events that have hit different Italian regions in recent years have highlighted the complexity of the challenge for the public bodies both in the emergency management and post‐event reconstruction and in the planning of effective risk prevention and mitigation measures to be implemented in ‘peacetime’. These difficulties concern, in particular, the capacity to properly manage the financial and technical resources available and to identify the intervention priorities throughout the entire emergency cycle. For correct management, the priority is to quantify and localize, through simulations, the quantification of probable damages and to evaluate in terms of cost‐benefits the possible alternative strategies for mitigation, also taking into account the potential, in terms of cost‐effectiveness, of integrated measures for seismic and energy retrofitting. In this framework, the project CAESAR II (Complementary Analyses for Emergency planning based on Seismic Risks impact evaluations) has been developed as a Decision Support System for Public Authorities in charge of developing Disaster Risk Reduction plans, with the possibility of programming mid to long‐term investments for public and private properties, as well as defining custom financial support mechanisms and tax incentives

Ontology-driven multicriteria decision support for victim evacuation

Abstract In light of the complexity of unfolding disasters, the diversity of rapidly evolving events, the enormous amount of generated information, and the huge pool of casualties, emergency responders (ERs) may be overwhelmed and in consequence poor decisions may be made. In fact, the possibility of transporting the wounded victims to one of several hospitals and the dynamic changes in healthcare resource availability make the decision process more complex. To tackle this problem, we propose a multicriteria decision support service, based on the Analytic Hierarchy Process (AHP) method, that aims to avoid overcrowding and outpacing the capacity of a hospital to effectively provide the best care to victims by finding out the most appropriate hospital that meets the victims’ needs. The proposed approach searches for the most appropriate healthcare institution that can effectively deal with the victims’ needs by considering the availability of the needed resources in the hospital, the victim’s wait time to receive the healthcare, and the transfer time that represents the hospital proximity to the disaster site. The evaluation and validation results showed that the assignment of hospitals was done successfully considering the needs of each victim and without overwhelming any single hospital

A Knowledge-Based Multi-Criteria Decision Support System Encompassing Cascading Effects For Disaster Management

This paper describes a knowledge-based decision support system (KB-DSS) to improve the preparedness of crisis situations induced by natural and technological hazards. The proposed KB-DSS aims to manage the potential cascading effects generated by a triggering hazard assessing the possible event time histories based on interconnected probabilistic simulation models. From a methodological point of view, a decision model based on two Multi-Criteria Decision-Making (MCDM) algorithms follows a cascading effect simulation model. This combination allows to support the decision maker in comparing a set of mitigation strategies on the basis of their expected impacts and his priorities. The algorithm is based on an ensemble approach, which combines decisions over an array of possible impact scenarios, instead of only relying on the average impact scenario. An application of the KB-DSS to the case of a possible reactivation of Nea Kameni volcano in Santorini is presented to show how the proposed architecture could be applied to a real case. The proposed methodology supports the emergency planners in making the best decisions supporting them also in the choice of the best timing for the intervention