Digital post-disaster risk management twinning: A review and improved conceptual framework

In this study, we addressed the difficulty of systematic and accurate identification and early warning of secondary disaster events after natural disasters. We analyzed the causes of common secondary disaster events, established the correlation between common everyday items and the types of secondary disasters, and constructed six secondary disaster factor datasets, namely, fire, flammable objects, explosive objects, toxic substances, trapped personnel, and dangerous buildings. We proposed a multi-model cluster decision method to extract the secondary disaster factors’ visual features, and we created a ResNet-CDMV image classification algorithm with higher accuracy recognition performance than the traditional single model. The experimental results show that the ResNet-CDMV algorithm in this study has an identification mAP value of 87% for secondary disaster factors. For this algorithm, Faster-RCNN, SSD, CornerNet, and CenterNet, the mAP value of the YOLOv7 object detection algorithm is increased by 9.333%, 11.833%, 13%, 11%, and 8.167%, respectively. Based on the systematic analysis of the formation mechanism of secondary disasters, the high-precision identification method built in this study is applied to the identification and early warning of secondary disasters, which is of great significance in reducing the occurrence of secondary disasters and ensuring the protection of life and property.

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Community Twin Ecosystem for Disaster Resilient Communities

Luleci,

Sevim,

Ozguven

et al. 2024

Smart Cities

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This paper presents COWINE (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWINE aims to enhance the disaster resilience of communities by fostering collaborative participation in the use of its DT among the decision-makers, the general public, and other involved stakeholders. COWINE leverages Cities:Skylines as its base simulation engine integrated with real-world data for community DT development. It is capable of capturing the dynamic, intricate, and interconnected structures of communities to provide actionable insights into disaster resilience planning. Through demonstrative, simulation-based case studies on Brevard County, Florida, the paper illustrates COWINE’s collaborative use with the involved parties in managing tornado scenarios. This study demonstrates how COWINE supports the identification of vulnerable areas, the execution of adaptive strategies, and the efficient allocation of resources before, during, and after a disaster. This paper further explores potential research directions using COWINE. The findings show COWINE’s potential to be utilized as a collaborative tool for community disaster resilience management.

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Digital post-disaster risk management twinning: A review and improved conceptual framework

Cited by 5 publications

References 205 publications

Reviewing the historical milestones of risk management

Reviewing the historical milestones of risk management

Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters

Community Twin Ecosystem for Disaster Resilient Communities

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