Optimizing Emergency Shelter Selection in Earthquakes Using a Risk-Driven Large Group Decision-Making Support System

This study presents a novel risk-based decision support system for helping disaster risk management planners select the best locations for emergency shelters after an earthquake. The system starts by identifying 18 criteria, based on stakeholder analysis, that are important for selecting shelter sit...

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Veröffentlicht in:Sustainability 2023-03, Vol.15 (5), p.4019
Hauptverfasser: Bakhshi Lomer, Amir Reza, Rezaeian, Mahdi, Rezaei, Hamid, Lorestani, Akbar, Mijani, Naeim, Mahdad, Mohammadreza, Raeisi, Ahmad, Arsanjani, Jamal Jokar
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Sprache:eng
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Zusammenfassung:This study presents a novel risk-based decision support system for helping disaster risk management planners select the best locations for emergency shelters after an earthquake. The system starts by identifying 18 criteria, based on stakeholder analysis, that are important for selecting shelter sites. These criteria are then standardized to reflect their importance in the site selection process. Next, a Large Group Decision-Making (LGDM) model is used to determine the weight of each criterion based on collective intelligence. Finally, the Ordered Weighted Average (OWA) method is used to assess the suitability of different geographical locations for emergency shelters, resulting in a suitability map. The factors that were most significant for selecting the best emergency shelters were the distance from the fault, population density, access to green spaces, and building quality. The area of the optimal sites for emergency shelters in the region varied depending on the decision-maker’s risk attitude, ranging from 4% in an extremely pessimistic scenario to 28% in an extremely optimistic scenario. This system combines Geographic Information Systems (GIS) and LGDM to help decision-makers identify the optimal sites for emergency shelters under different risk levels, which can contribute to better-informed decision-making regarding disaster resilience.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15054019