Process-Guided Data-Driven modeling of water temperature: Anchoring predictions with thermodynamic constraints in the Big Data era
- ADS bibcode
- 2018AGUFMIN41D0872R
- year
- 2018
- Listed Authors
- Read, J. S.
- Willard, J.
- Jia, X.
- Karpatne, A.
- Appling, A.
- Zwart, J. A.
- Oliver, S.
- Watkins, W. D.
- Hansen, G. J.
- Kumar, V.
- Listed Institutions
- Data Science Branch, USGS Integrated Information Dissemination Division, Middleton, WI, United States
- University of Minnesota Twin Cities, Minneapolis, United States
- University of Minnesota Twin Cities, Minneapolis, MN, United States
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
- Data Science Branch, USGS Integrated Information Dissemination Division, Middleton, WI, United States
- Data Science Branch, USGS Integrated Information Dissemination Division, Middleton, WI, United States
- USGS Wisconsin Water Science Center, Middleton, United States
- Data Science Branch, USGS Integrated Information Dissemination Division, Middleton, United States
- Department of Natural Resources Minnesota, Saint Paul, MN, United States
- University of Minnesota Twin Cities, Minneapolis, MN, United States
Linked Authors [?]
Linked Institutions