Partial differential equation driven dynamic graph networks for predicting stream water temperature
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Abstract
This paper presents a physics-guided machine learning approach that incorporates partial differential equations (PDEs) in a graph neural network model to improve the prediction of water temperature in river networks. The standard graph neural network model often uses pre-defined edge weights based on distance or similarity measures. Such static graph structure can be limited in capturing multiple processes in a physical system that interact and evolve over time. The limitation to represent underlying physical processes can severely affect the performance of the predictive model, especially when we have access to limited training data. To better capture the dynamic interactions among multiple segments in a river network, we built a dynamic graph model, where the graph structure is driven by the PDE that describes underlying physical processes. We further combine the dynamic graph structure and the recurrent layers to model temporal dependencies and improve the prediction. We demonstrate the effectiveness of the proposed method in a subnetwork of the Delaware River Basin. In particular, we show that the proposed method outperforms existing physics-based and machine learning models in temperature prediction using sparse observation data for training. The proposed method has also been shown to produce better performance when generalized to different seasons.
Suggested Citation
Bao, T., Jia, X., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., Johnson, T.T., 2021, Partial differential equation driven dynamic graph networks for predicting stream water temperature, in 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, December 7-10, 2021, p. 11-20, https://doi.org/10.1109/ICDM51629.2021.00011.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Partial differential equation driven dynamic graph networks for predicting stream water temperature |
| DOI | 10.1109/ICDM51629.2021.00011 |
| Year Published | 2021 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers |
| Contributing office(s) | WMA - Integrated Information Dissemination Division |
| Description | 10 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | 2021 IEEE International Conference on Data Mining (ICDM) |
| First page | 11 |
| Last page | 20 |
| Conference Title | IEEE International Conference on Data Mining (ICDM) |
| Conference Location | Auckland, New Zealand |
| Conference Date | December 7-10, 2021 |