Morphodynamic simulations require repeated coupling between hydrodynamic solvers, sediment transport modules, and bed updating.
For long duration, multi-scenario simulations on large unstructured meshes, such coupling is computationally demanding.
This study proposes an Adaptive-Horizon Graph Neural Operator (AH-GNO)
That formulates bed evolution as a graph operator learning problem from recent hydrodynamic and topographic states to predict future bed elevation increments.
The model operates directly on the native unstructured meshes.
The approach integrates area-weighted graph learning, historical state selection, and forecast horizon estimation, allowing the effective history length (K) and update interval (B) to vary with the evolving flow and bed state.
Using Telemac2D/GAIA solutions as supervised numerical references, the framework is evaluated on the official Bump and Yen bend benchmarks and on a field-scale application (Waiho River, New Zealand).
Results show that the AH-GNO reproduces the main patterns of bedform migration, bend-scale erosion and deposition, and localized bed adjustment.
Across the three cases, the tested CPU-GPU deployment reduced post-training bed-update workflow time from 12~1204 s with GAIA to 0.08~3.34 s with AH-GNO.
This improvement corresponds to deployment-specific accelerations of approximately 150~360 times relative to 4-core GAIA CPU configurations, and with 105~186 times gains for 20-core CPU configurations.
These results suggest that, while retaining hydrodynamic feedback from TELEMAC-2D, a learned operator for bed updating can provide an efficient acceleration component for morphodynamic scenario analysis and rapid coupled simulation.