Digital Risk Twins: The Next Generation of Digital Twins for Complex Disaster Scenarios

Disaster risk management (DRM) faces increasing challenges due to urbanisation, environmental degradation, and the growing complexity of interacting hazards. Digital Twins (DTs), defined as digital representations of physical systems connected through continuous data exchange, have gained attention for their potential to support monitoring, simulation, and decision-making. However, their application to disaster contexts remains limited, as many DT implementations depend on uninterrupted automated data streams, predefined control mechanisms, and automated interventions that are often unavailable or impractical during disasters.

In this study, the Digital Risk Twin (DRT) is introduced as a paradigm specifically designed for DRM. The DRT extends DT concepts by integrating automated and manual data collection methods, such as IoT, remote sensing, surveys and field observations, while incorporating human-in-the-loop decision-making for flexible and effective interventions, maintaining real-time virtual simulations, and addressing disaster scenario challenges. To demonstrate its practical relevance, an example of how a DRT can be conceptualised for a multi-hazard response case study is formulated, illustrating how DRT can support effective DRM.

The DRT integrates diverse data sources such as remote sensing, in situ observations, field surveys, and community-based reporting, while supporting both automated analysis and expert-driven interpretation. A defining feature of the framework is the explicit inclusion of human decision-making within the digital representation. Rather than aiming for full automation, the DRT enables iterative interaction between digital models and stakeholders, supporting context-aware decisions under uncertainty. This is particularly important in disaster situations where data gaps, infrastructure damage, and rapidly changing conditions constrain the effectiveness of purely automated systems.

Digital Risk Twins represent a conceptual advancement over original Digital Twins by addressing the socio-technical nature of disaster risk. The proposed framework and multi-hazard conceptualisation provide a foundation for future operational implementations, with the potential to strengthen adaptive capacity and resilience to cascading and compound hazards.