A physics-informed machine learning framework for event-scale urban flood intensity prediction at the sub-seasonal time scales in data-scarce cities

Urban flooding poses persistent challenges in rapidly urbanizing regions, where the lack of ground-based observations limits both flood characterization and predictive modeling. In many flood-prone cities, physics-based hydrologic and hydrodynamic models are constrained by the availability of high-resolution drainage data, boundary conditions, and event-specific calibration, limiting their applicability for rapid or scalable urban flood assessment. To address this gap, we present a scalable, physics-informed machine learning framework for predicting event-scale urban flood intensity at 10 m resolution and at the sub-seasonal time scales without reliance on ground flood calibration.

The proposed approach integrates multi-sensor information, including SAR-derived flood intensity, with high-resolution hydro-meteorological and geospatial predictors that encode rainfall forcing, terrain controls, urban morphology, and surface imperviousness. A key component of the framework is the use of physically interpretable static predictors, such as height above nearest drainage (HAND), to represent drainage proximity and inundation potential, thereby introducing hydrologically meaningful constraints into the learning process. Flooding is modeled as a continuous spatial variable rather than a binary state, enabling a more realistic representation of flood severity and spatial heterogeneity across urban landscapes.

The framework is applied to Mumbai, India, as a primary testbed and evaluated across multiple rainfall-driven flood events. Model performance is assessed through cross-event consistency and spatial generalization, internal agreement with independently derived flood intensity patterns, and coherence with known flood-prone zones shaped by drainage networks and urban form. Results demonstrate stable and physically plausible flood intensity predictions across events without local recalibration, highlighting the framework’s capacity to generalize in the absence of in situ flood measurements.

By combining observation-driven learning with physically informed predictors, this work advances a transferable pathway for high-resolution urban flood intensity prediction in data-scarce environments. The proposed framework is intended to support scalable flood risk assessment and early-stage decision-making in rapidly urbanizing regions facing increasing flood hazards.