Accelerating Urban Flood Data Assimilation: Coupling Physics Guided AI Emulators with Real Time Observations

Timely and reliable urban flood forecasting is essential for mitigating damage and supporting emergency decision-making. Ensemble data assimilation can improve forecast reliability by updating model states with observations, but real-time use with high-resolution hydrodynamic models is often constrained by computational cost. We propose an integrated forecasting framework that couples a physics-guided AI emulator with data assimilation to enable efficient, high-resolution spatiotemporal inundation prediction. The emulator is trained on high-fidelity hydrodynamic simulations and reproduces key flood dynamics with substantially lower runtime than conventional solvers, allowing large ensembles generated by perturbing initial conditions and meteorological forcings to quantify uncertainty. Real-time inundation-depth observations are assimilated to update evolving flood states, using both synthetic data for controlled testing and ground-based depth information derived from surveillance-camera imagery for real-event conditions. The framework is applied for an urban drainage basin in Seoul, South Korea. The presentation will discuss key challenges for real-time urban flood assimilation, including observation uncertainty and representativeness, intermittent availability and latency, and the balance between ensemble size and update frequency. We also examine how emulator design affects physical consistency during assimilation and outline remaining limitations for operational deployment.