Physically Anchored Multi-Resolution Neural Operator Framework for Flood Inundation Prediction

Abstract. Accurate flood inundation modeling using high-resolution hydrodynamic simulations is computationally demanding, limiting their use for large-scale analysis and rapid scenario evaluation. Although machine learning surrogates have been developed, many struggle to reproduce the full spatiotemporal evolution of flood dynamics while maintaining physical consistency across spatial scales. In particular, simultaneously capturing basin-scale wave propagation and fine-scale inundation boundaries remains challenging. This study presents a multi-resolution deep learning framework for dynamic flood prediction. The approach combines a coarse-resolution neural operator that captures large-scale hydrodynamic behavior with a terrain-aware refinement module that reconstructs a fine-scale boundary structure. The framework is trained on high-fidelity two-dimensional shallow-water simulations and evaluated across riverine, dam-break, and complex floodplain systems, including tests under structured bathymetric uncertainty. Results demonstrate accurate reconstruction of continuous water depth fields, wet-dry delineation, and peak flow magnitude and timing. The model preserves the evolution of domain-integrated water volume over time, ensuring physically consistent mass dynamics rather than purely geometric agreement, and maintains probabilistic consistency when input topography is uncertain. The framework, therefore, provides high-resolution flood predictions at substantially reduced computational cost relative to direct high-resolution simulation. These findings show that multi-resolution deep learning can approximate hydrodynamic flood processes with strong physical fidelity and robustness to geometric uncertainty, supporting scalable flood hazard assessment and rapid predictive modeling.