Process‐Guided Deep Learning Predictions of Lake Water Temperature-Reference-Cited by-同舟云学术

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Process‐Guided Deep Learning Predictions of Lake Water Temperature

Published:2019-11 Issue:11 Volume:55 Page:9173-9190
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Read Jordan S.¹^ORCID,Jia Xiaowei²,Willard Jared²,Appling Alison P.¹^ORCID,Zwart Jacob A.¹^ORCID,Oliver Samantha K.¹^ORCID,Karpatne Anuj³,Hansen Gretchen J. A.⁴^ORCID,Hanson Paul C.⁵^ORCID,Watkins William¹^ORCID,Steinbach Michael²^ORCID,Kumar Vipin²

Affiliation:

1. U.S. Geological Survey Reston VA USA

2. Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA

3. Department of Computer Science Virginia Tech Blacksburg VA USA

4. Department of Fisheries, Wildlife, and Conservation Biology University of Minnesota Minneapolis MN USA

5. Center for Limnology University of Wisconsin‐Madison Madison WI USA

Funder

National Stroke Foundation

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR024922

Reference86 articles.

1. Evaluation of the current state of mechanistic aquatic biogeochemical modeling

2. The Distribution and Prediction of Summer Near-Surface Water Temperatures in Lakes of the Coterminous United States and Southern Canada

3. Regional coherence of climatic and lake thermal variables of four lake districts in the Upper Great Lakes Region of North America

4. Energetic Responses of Salmon to Temperature. A Study of Some Thermal Relations in the Physiology and Freshwater Ecology of Sockeye Salmon (Oncorhynchus nerkd)

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