Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks-Reference-Cited by-同舟云学术

Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Chen Shengyu¹,Zwart Jacob A.²,Jia Xiaowei¹

Affiliation:

1. University of Pittsburgh, Pittsburgh, PA, USA

2. U.S. Geological Survey, Pittsburgh, PA, USA

Funder

NSF (National Science Foundation)

USGS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539115

Reference46 articles.

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2. Nans Addor et al. 2017. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrology and Earth System Sciences (2017). Nans Addor et al. 2017. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrology and Earth System Sciences (2017).

3. Luca Bertinetto et al. 2018. Meta-learning with differentiable closed-form solvers. arXiv preprint arXiv:1805.08136 (2018). Luca Bertinetto et al. 2018. Meta-learning with differentiable closed-form solvers. arXiv preprint arXiv:1805.08136 (2018).

4. A manifesto for the equifinality thesis

5. Shengyu Chen et al. 2021. Heterogeneous stream-reservoir graph networks with data assimilation. In ICDM. Shengyu Chen et al. 2021. Heterogeneous stream-reservoir graph networks with data assimilation. In ICDM.

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