Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting

• URL: http://arxiv.org/abs/2507.15614v1
• Date: Mon, 21 Jul 2025 13:38:54 GMT
• Title: Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
• Authors: Edward Holmberg, Pujan Pokhrel, Maximilian Zoch, Elias Ioup, Ken Pathak, Steven Sloan, Kendall Niles, Jay Ratcliff, Maik Flanagin, Christian Guetl, Julian Simeonov, Mahdi Abdelguerfi,
• Abstract summary: This paper introduces a deep learning surrogate that treats HEC-RAS not as a solver but as a data-generation engine.<n>Trained on 67 reaches of the Mississippi River Basin, the surrogate was evaluated on a year-long, unseen hold-out simulation.<n>Results show the model achieves a strong predictive accuracy, with a median absolute stage error of 0.31 feet.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physics-based solvers like HEC-RAS provide high-fidelity river forecasts but are too computationally intensive for on-the-fly decision-making during flood events. The central challenge is to accelerate these simulations without sacrificing accuracy. This paper introduces a deep learning surrogate that treats HEC-RAS not as a solver but as a data-generation engine. We propose a hybrid, auto-regressive architecture that combines a Gated Recurrent Unit (GRU) to capture short-term temporal dynamics with a Geometry-Aware Fourier Neural Operator (Geo-FNO) to model long-range spatial dependencies along a river reach. The model learns underlying physics implicitly from a minimal eight-channel feature vector encoding dynamic state, static geometry, and boundary forcings extracted directly from native HEC-RAS files. Trained on 67 reaches of the Mississippi River Basin, the surrogate was evaluated on a year-long, unseen hold-out simulation. Results show the model achieves a strong predictive accuracy, with a median absolute stage error of 0.31 feet. Critically, for a full 67-reach ensemble forecast, our surrogate reduces the required wall-clock time from 139 minutes to 40 minutes, a speedup of nearly 3.5 times over the traditional solver. The success of this data-driven approach demonstrates that robust feature engineering can produce a viable, high-speed replacement for conventional hydraulic models, improving the computational feasibility of large-scale ensemble flood forecasting.

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