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Physics-guided recurrent graph model for predicting flow and temperature in river networks

By: , , , , , , , , , , and 
https://doi.org/10.1137/1.9781611976700.69

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Abstract

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.
Publication type Conference Paper
Publication Subtype Conference Paper
Title Physics-guided recurrent graph model for predicting flow and temperature in river networks
DOI 10.1137/1.9781611976700.69
Year Published 2021
Language English
Publisher Society for Industrial and Applied Mathematics
Contributing office(s) WMA - Integrated Information Dissemination Division
Description 7 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)
First page 612
Last page 620
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