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

Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2021
PublisherSiam Society
Pages612-620
Number of pages9
ISBN (Electronic)9781611976700
StatePublished - 2021
Event2021 SIAM International Conference on Data Mining, SDM 2021 - Virtual, Online
Duration: Apr 29 2021May 1 2021

Publication series

NameSIAM International Conference on Data Mining, SDM 2021

Conference

Conference2021 SIAM International Conference on Data Mining, SDM 2021
CityVirtual, Online
Period4/29/215/1/21

Bibliographical note

Funding Information:
This research was supported by the NSF award 1934721. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Publisher Copyright:
© 2021 by SIAM.

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