A Scenario-Driven Fault-Control Decision Support Model for Disaster Preparedness Using Case-Based Reasoning

Abstract

To reduce disaster response failures, controlling disaster preparedness faults is essential. Emergency management departments should analyze the lack of capability as soon as an early warning is released and make adaptive improvements based on disaster risk information. However, because of time constraints and experience scarcity, it frequently is challenging for decision makers to identify faults precisely and develop suitable fault-control measures. Current studies lack attention to this aspect. Case-based reasoning (CBR) can rapidly acquire relevant knowledge for target case by learning from historical cases. Therefore, this paper adopts CBR to support fault-control decision-making when a disaster is approaching. We introduce the cause–effect bow-tie diagram to reconstruct an ontology-supported fault-control scenario model. Based on the structured scenario, a multiphase scenario retrieval is presented to recommend similar scenarios. To generate the target fault-control measures, a hybrid scenario reuse is carried out to adjust the previous fault-control measures in similar scenarios. Finally, a case study illustrates the proposed model’s use in a typhoon situation. The results show that scenario-driven CBR is a proactive and agile approach for providing fault-control suggestions.