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.
View on arXiv@article{zoch2025_2503.16850,
title={ Physics-Informed Neural Network Surrogate Models for River Stage Prediction },
author={ Maximilian Zoch and Edward Holmberg and Pujan Pokhrel and Ken Pathak and Steven Sloan and Kendall Niles and Jay Ratcliff and Maik Flanagin and Elias Ioup and Christian Guetl and Mahdi Abdelguerfi },
journal={arXiv preprint arXiv:2503.16850},
year={ 2025 }
}