Transforming resilience with predictive digital twin technologies

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.