Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity
This work addresses urban resilience and equity for cities facing climate hazards, but it is incremental as it builds on existing digital twin and modeling approaches with a focus on equity-aware analytics.
The paper tackles the problem of climate hazards like wildfires and heatwaves affecting urban stability and equity by presenting a Hazard-Responsive Digital Twin (H-RDT) that maintains stable indoor temperature predictions (31-33°C) under sensor loss and reduces population-weighted thermal risk by 11-13% through interventions like cooling-center activation.
Compounding climate hazards, such as wildfire-induced outages and urban heatwaves, challenge the stability and equity of cities. We present a Hazard-Responsive Digital Twin (H-RDT) that combines physics-informed neural network modeling, multimodal data fusion, and equity-aware risk analytics for urban-scale response. In a synthetic district with diverse building archetypes and populations, a simulated wildfire-outage-heatwave cascade shows that H-RDT maintains stable indoor temperature predictions (approximately 31 to 33 C) under partial sensor loss, reproducing outage-driven surges and recovery. The reinforcement learning based fusion module adaptively reweights IoT, UAV, and satellite inputs to sustain spatiotemporal coverage, while the equity-adjusted mapping isolates high-vulnerability clusters (schools, clinics, low-income housing). Prospective interventions, such as preemptive cooling-center activation and microgrid sharing, reduce population-weighted thermal risk by 11 to 13 percent, shrink the 95th-percentile (tail) risk by 7 to 17 percent, and cut overheating hours by up to 9 percent. Beyond the synthetic demonstration, the framework establishes a transferable foundation for real-city implementation, linking physical hazard modeling with social equity and decision intelligence. The H-RDT advances digital urban resilience toward adaptive, learning-based, and equity-centered decision support for climate adaptation.