Digital Twin Paradigm for Disaster Resilience
Digital twin is recognized as digital copies of physical world’s objects stored in digital space and utilized to simulate the sequences and consequences of target phenomena. By incorporating physical world’s data into the digital twin, developers and users have a full view of the target through real-time feedback. Recent advances in high-performance computing and large-scale data fusion of sensing and observations of both natural and social phenomena are enhancing applicability of digital twin paradigm to natural disaster research. Artificial intelligence (AI) and machine learning are also being applied more and more widely across the world and contributing as essential elements of digital twin. Those have significant implications for disaster response and recovery to hold out the promise of dramatically improving our understanding of disaster-affected areas and responses in real-time. Given the importance of the implications of constructing digital twin for disaster research and society’s resilience, we convene the session titled “Digital Twin Paradigm for Disaster Resilience” and aim to provide an opportunity to share the advances of monitoring, sensing, simulation, forecasting, mapping of natural and social systems in disaster process that comprise the disaster digital twin and discuss the future perspectives of utilizing it as a powerful tool for enhancing disaster resilience. We encourage the participation from diverse fields of natural disasters, not only academia and developers, but also responders and practitioners to discuss the future of the digital twin paradigm for disaster resilience.
Organized by: International Research Institute of Disaster Science, Tohoku University
