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.
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Data Availability Statement
The case data used in the case study are proprietary or confidential in nature and may only be provided with restrictions. Some case data are not publicly available due to privacy restrictions, and other case data are available after data preprocessing. All the related models are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 72274123, 71904121, 72134005, and 72004135); the Science and Technology Commission of Shanghai Municipality (Grant Nos. 23692116200 and 22692195800); and the China Postdoctoral Science Foundation (Grant No. 2019M661531). Many thanks to the editors and reviewers for their valuable comments.
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Published In
Natural Hazards Review
Volume 24 • Issue 4 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 1, 2022
Accepted: May 23, 2023
Published online: Jul 27, 2023
Published in print: Nov 1, 2023
Discussion open until: Dec 27, 2023
ASCE Technical Topics:
- Case studies
- Disaster preparedness
- Disaster response
- Disaster risk management
- Disaster warning systems
- Disasters and hazards
- Emergency management
- Engineering fundamentals
- Geotechnical engineering
- Geotechnical investigation
- Information management
- Man-made disasters
- Methodology (by type)
- Penetration tests
- Research methods (by type)
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