Exploring graph neural networks for flood modeling: Challenges, opportunities, and future prospects

Hydrodynamic flood models play a central role in understanding the spatiotemporal evolution of floods for effective disaster management. Current implementations are limited, as physics-based numerical models are constrained by slow inference times, and data-driven models lack interpretability and physical consistency. Graph Neural Networks (GNNs) provide a physically meaningful framework to represent unstructured spatial domains as graphs, mirroring the node–link structure of numerical models. Unlike other deep learning methods, GNNs require no complex spatial data transformation and can seamlessly integrate physics-based priors. This review synthesizes forty nine peer-reviewed studies on GNN applications in flood modeling, identifying key gaps in physics incorporation, scalability, and cross-domain generalization. Drawing on advances from related fields where GNNs have excelled, we outline strategies to overcome these limitations. GNNs hold significant potential as next-generation flood models by combining physical realism, computational efficiency, and adaptability for real-time flood prediction and risk management.