Physics-Informed Neural Network Surrogate Models for River Stage Prediction

• URL: http://arxiv.org/abs/2503.16850v1
• Date: Fri, 21 Mar 2025 04:48:22 GMT
• Title: Physics-Informed Neural Network Surrogate Models for River Stage Prediction
• Authors: Maximilian Zoch, Edward Holmberg, Pujan Pokhrel, Ken Pathak, Steven Sloan, Kendall Niles, Jay Ratcliff, Maik Flanagin, Elias Ioup, Christian Guetl, Mahdi Abdelguerfi,
• Abstract summary: PINNs can successfully approximate HEC-RAS numerical solutions when trained on a single river.<n>We evaluate the model's performance in terms of accuracy and computational speed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work investigates the feasibility of using Physics-Informed Neural Networks (PINNs) as surrogate models for river stage prediction, aiming to reduce computational cost while maintaining predictive accuracy. Our primary contribution demonstrates that PINNs can successfully approximate HEC-RAS numerical solutions when trained on a single river, achieving strong predictive accuracy with generally low relative errors, though some river segments exhibit higher deviations. By integrating the governing Saint-Venant equations into the learning process, the proposed PINN-based surrogate model enforces physical consistency and significantly improves computational efficiency compared to HEC-RAS. We evaluate the model's performance in terms of accuracy and computational speed, demonstrating that it closely approximates HEC-RAS predictions while enabling real-time inference. These results highlight the potential of PINNs as effective surrogate models for single-river hydrodynamics, offering a promising alternative for computationally efficient river stage forecasting. Future work will explore techniques to enhance PINN training stability and robustness across a more generalized multi-river model.

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