This research examines the role of digital twin technology in enhancing disaster preparedness and response frameworks, with a focus on scenarios involving tsunamis, earthquakes, and floods. The primary objective was to evaluate how digital twins integrate real-time data, predictive modelling, and stakeholder engagement to enhance resilience. A systematic literature review was conducted in accordance with PRISMA guidelines, screening 342 studies and narrowing the selection to 120 high-quality sources that met the inclusion criteria. The analysis revealed that digital twin models improved forecast accuracy by an average of 28% compared to traditional disaster models, particularly in tsunami inundation mapping and urban flood simulations. Community engagement through interactive platforms was reported in 62% of the reviewed cases, with direct evidence of faster evacuation and resource allocation. Post-disaster recovery applications demonstrated measurable efficiency gains, reducing infrastructure restoration times by approximately 15%. However, data gaps and interoperability issues were identified as recurring limitations, contributing to an estimated error margin of 8–12% in predictive outputs. Overall, the findings confirm that digital twins offer a transformative pathway for proactive disaster management. While challenges in data quality and governance remain, their integration into national frameworks could significantly enhance both preparedness and resilience.
Elemure et al. (Tue,) studied this question.