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Title: Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks
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.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Demeniconi, Carlotta; Davidson, Ian
Date Published:
Journal Name:
Proceedings of the 2021 SIAM International Conference on Data Mining
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
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