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. Given its flexible input and architecture, GNNs can be leveraged alongside physics-informed techniques with ease, significantly improving interpretability and generalizability. We introduce a novel flood GNN architecture, DUALFloodGNN, which embeds physical constraints at both global and local scales through explicit loss terms. The model jointly predicts water volume at nodes and flow along edges through a shared message-passing framework. To improve performance for autoregressive inference, model training is conducted with a multi-step loss enhanced with dynamic curriculum learning. Compared with standard GNN architectures and state-of-the-art GNN flood models, DUALFloodGNN achieves substantial improvements in predicting multiple hydrologic variables (e.g., water volume, flow, and depth) while maintaining high computational efficiency. The model is open sourced at https://github.com/acostacos/dual_flood_gnn. The dataset is open sourced at https://hdl.handle.net/2123/35293 with the DOI 10.25910/9xav-0s86.

翻译：洪水模型通过模拟洪水的时空水动力学过程为战略性灾害管理提供决策支持。基于物理机制的数值洪水模型虽具高精度，但其高昂的计算成本限制了其在需要快速预测的业务化场景中的应用。采用图神经网络（GNN）设计的模型兼具运算速度与预测精度，同时具备处理非结构化空间域的能力。凭借灵活的输入结构与架构设计，GNN能够便捷地结合物理信息增强方法，显著提升模型的可解释性与泛化能力。我们提出了一种新型洪水GNN架构DUALFloodGNN，通过显式损失项在全局与局部尺度嵌入物理约束。该模型通过共享消息传递框架联合预测节点处的水量及边上的流量。为提升自回归推理性能，模型训练采用结合动态课程学习的多步损失函数。与标准GNN架构及前沿GNN洪水模型相比，DUALFloodGNN在预测多类水文变量（如水量、流量和水深）时取得显著性能提升，同时保持较高计算效率。模型开源地址为https://github.com/acostacos/dual_flood_gnn，数据集开源地址为https://hdl.handle.net/2123/35293（DOI: 10.25910/9xav-0s86）。