Real-Time, Crowdsourcing-Enhanced Forecasting of Building Functionality During Urban Floods

Urban flood emergency response increasingly relies on infrastructure impact forecasts rather than hazard variables alone. However, real-time predictions are unreliable due to biased rainfall, incomplete flood knowledge, and sparse observations. Conventional open-loop forecasting propagates impacts without adjusting the system state, causing errors during critical decisions. This study presents CRAF (Crowdsourcing-Enhanced Real-Time Awareness and Forecasting), a physics-supervised, closed-loop framework that converts sparse human-sensed evidence into rolling impact forecasts. By coupling physics-based simulation learning with crowdsourced observations, CRAF infers system conditions from incomplete data and propagates them forward to produce multi-step, real-time predictions of zone-level building functionality loss without online retraining. This closed-loop design supports continuous state correction and forward prediction under weakly structured data with low-latency operation. Offline evaluation demonstrates stable performance across simulation-based storm scenarios. In real-event application during Typhoon Haikui (2023) in Fuzhou, China, CRAF reduces 0.5–2.5 h ahead forecast errors by 85%–95% relative to fixed rainfall-driven forecasting and by 75%–80% relative to updated rainfall-driven forecasting, while outperforming a conventional Ensemble Kalman Filter–based data-assimilation baseline and limiting computation to approximately 10 minutes per update cycle. Sensitivity analysis further shows that the framework remains stable under 10%–30% perturbation or missingness in accepted crowdsourced observations. These results show that impact-state alignment—rather than hazard refinement alone—is essential for reliable real-time decision support, providing a pathway toward operational digital twins for resilient urban infrastructure systems.