Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity

TL;DR

This paper introduces a hazard-responsive digital twin that integrates physics-informed neural networks, multimodal data fusion, and equity analytics to improve urban resilience and equity against climate hazards like wildfires and heatwaves.

Contribution

It presents a novel framework combining physical modeling, adaptive data fusion, and social equity considerations for climate-driven urban resilience.

Findings

Maintains stable indoor temperature predictions under sensor loss.

Reduces thermal risk and overheating hours through targeted interventions.

Establishes a transferable foundation for real-city implementation.

Abstract

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.