Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks

• URL: http://arxiv.org/abs/2009.12575v2
• Date: Tue, 8 Dec 2020 18:00:55 GMT
• Title: Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
• Authors: Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, and Vipin Kumar
• Abstract summary: We build a recurrent graph network model to capture the interactions among multiple segments in the river network. We then transfer knowledge from physics-based models to initialize the machine learning model and learn the physics of streamflow and thermodynamics. We show that the proposed method brings a 33%/14% improvement over the state-of-the-art physics-based model.
• Score: 3.80455319348953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we present a pre-training technique which transfers knowledge from physics-based models to initialize the machine learning model and learn the physics of streamflow and thermodynamics. We also propose a new loss function that balances the performance over different river segments. We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin. In particular, we show that the proposed method brings a 33\%/14\% improvement over the state-of-the-art physics-based model and 24\%/14\% over traditional machine learning models (e.g., Long-Short Term Memory Neural Network) in temperature/streamflow prediction using very sparse (0.1\%) observation data for training. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.

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