DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling

ArXi:2512.23964v2 Announce Type: replace-cross Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical flood models are accurate, their substantial computational cost limits their use in operational settings where rapid predictions are essential. Models designed with graph neural networks (GNNs) provide both speed and accuracy while having the ability to process unstructured spatial domains.