Jacob Aaron Zwart Jeffrey Michael Sadler Alison P. Appling Samantha K. Oliver Steven L. Markstrom Jared Willard Shaoming Xu Michael Steinbach Jordan Read Vipin Kumar Xiaowei Jia 2021 <div id="abstracts" data-extent="frontmatter"><div class="core-container"><div>This paper proposes a physics-guided machine learning approach that combines 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 transfer knowledge from physics-based models to guide the learning of the machine learning model. 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, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.</div></div></div> application/pdf 10.1137/1.9781611976700.69 en Society for Industrial and Applied Mathematics Physics-guided recurrent graph model for predicting flow and temperature in river networks text