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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

Keywords: Physics-Informed Neural Networks; Surrogate Modeling; River Stage Prediction; HEC-RAS

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. We demonstrate PINNs successfully approximate Hydrologic Engineering Center’s River Analysis System (HEC-RAS) solutions, achieving strong predictive accuracy, despite some variation among river segments. 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.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2025

Publication date: May 18, 2025

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-68558-282-1

Location: Nice, France

Dates: from May 18, 2025 to May 22, 2025