Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature
By: Xiaowei Jia, Shengyu Chen, Yiqun Xie, Haoyu Yang, Alison P. Appling, Samantha K. Oliver, and Zhe Jiang
Edited by: Arindam Banerjee, Zhi-Hua Zhou, Evangelos E. Papalexakis, and Matteo Riondato
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Abstract
This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments. In this paper, we first build a state-aware graph model to represent the interactions amongst streams and reservoirs, and then propose a parallel learning structure to extract the reservoir release information and use it to improve the prediction. In particular, for reservoirs with no available release information, we mimic the water managers' release decision process through a pseudo-prospective learning method, which infers the release information from anticipated water temperature dynamics. For reservoirs with the release information, we leverage a physics-based model to simulate the water release temperature and transfer such information to guide the learning process for other reservoirs. The evaluation for the Delaware River Basin shows that the proposed method brings over 10% accuracy improvement over existing data-driven models for stream temperature prediction when the release data is not available for any reservoirs. The performance is further improved after we incorporate the release data and physical simulations for a subset of reservoirs.
Suggested Citation
Jia, X., Chen, S., Xie, Y., Yang, H., Appling, A.P., Oliver, S.K., and Jiang, Z., 2022, Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature, in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), Alexandria, Virginia, United States, April 28-30, 2022, p. 91-99, https://doi.org/10.1137/1.9781611977172.11.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature |
| DOI | 10.1137/1.9781611977172.11 |
| Publication Date | April 20, 2022 |
| Year Published | 2022 |
| Language | English |
| Publisher | Society for Industrial and Applied Mathematics |
| Contributing office(s) | WMA - Integrated Modeling and Prediction Division |
| Description | 9 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) |
| First page | 91 |
| Last page | 99 |
| Conference Title | 2022 SIAM International Conference on Data Mining (SDM) |
| Conference Location | Alexandria, Virginia, United States |
| Conference Date | April 28-30, 2022 |