[2604.02876] Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting

Computer Science > Machine Learning arXiv:2604.02876 (cs) [Submitted on 3 Apr 2026] Title:Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting Authors:Valentin Mercier (Toulouse INP, IRIT, EPE UT), Serge Gratton (IRIT, EPE UT, Toulouse INP), Lapeyre Corentin (NVIDIA), Gwenaël Chevallet View a PDF of the paper titled Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting, by Valentin Mercier (Toulouse INP and 7 other authors View PDF Abstract:Operational flood forecasting still relies on high-fidelity two-dimensional hydraulic solvers, but their runtime can be prohibitive for rapid decision support on large urban floodplains. In parallel, AI-based surrogate models have shown strong potential in several areas of computational physics for accelerating otherwise expensive high-fidelity simulations. We address this issue on the lower Têt River (France), starting from a production-grade Telemac2D model defined on a high-resolution unstructured finite-element mesh with more than $4\times 10^5$ nodes. From this setup, we build a learning-ready database of synthetic but operationally grounded flood events covering several representative hydrograph families and peak discharges. On top of this database, we develop a graph-neural surrogate based on projected meshes and multimesh connectivity. The projected-mesh strategy keeps training tractable while preserving high-fidelity supervision from the original Telemac simulations, and the mu...