Physics-Informed Neural Network Surrogate Models for River Stage Prediction
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908276789084160 |
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| author | Zoch, Maximilian Holmberg, Edward Pokhrel, Pujan Pathak, Ken Sloan, Steven Niles, Kendall Ratcliff, Jay Flanagin, Maik Ioup, Elias Guetl, Christian Abdelguerfi, Mahdi |
| author_facet | Zoch, Maximilian Holmberg, Edward Pokhrel, Pujan Pathak, Ken Sloan, Steven Niles, Kendall Ratcliff, Jay Flanagin, Maik Ioup, Elias Guetl, Christian Abdelguerfi, Mahdi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Network Surrogate Models for River Stage Prediction Zoch, Maximilian Holmberg, Edward Pokhrel, Pujan Pathak, Ken Sloan, Steven Niles, Kendall Ratcliff, Jay Flanagin, Maik Ioup, Elias Guetl, Christian Abdelguerfi, Mahdi Machine Learning Artificial Intelligence 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. |
| title | Physics-Informed Neural Network Surrogate Models for River Stage Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.16850 |