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Abstract: Progress toward the coordinated development of machine learning water quality models at the CONUS extent (AGU25)

Progress toward the coordinated development of machine learning water quality models at the CONUS extent

Galen Gorski1, Jeremy Diaz1, Jared David Smith2, Alison Appling3, Samantha Oliver4, Ken Eng5, Lauren Koenig6, Wallace Anderson McAliley7, Kyle Compare8, Janet Barclay9, Ellie White10, Jacob A Zwart11, Christopher T Green12, Jeffrey Kwang13 and Scott Ator14, (1)U.S. Geological Survey, Water Mission Area, Reston, United States, (2)Cornell University, Civil and Environmental Engineering, Ithaca, United States, (3)University of Wisconsin Madison, Madison, United States, (4)U.S. Geological Survey, Upper Midwest Water Science Center, Madison, United States, (5)U.S. Geological Survey, Water Resources Mission Area, Reston, United States, (6)U.S. Geological Survey, Water Resources Mission Area, Troy, NY, United States, (7)Colorado School of Mines, Golden, United States, (8)U.S. Geological Survey, Water Resources Mission Area, Boise, United States, (9)United States Geological Survey, Water Resources Mission Area, East Hartford, United States, (10)USGS Integrated Information Dissemination Division, Data Science Branch, Reston, United States, (11)USGS Integrated Information Dissemination Division, Data Science Branch, San Francisco, United States, (12)U.S. Geological Survey, Integrated Modeling Prediction Division, Moffett Field, California, United States, (13)U.S. Geological Survey, Water Mission Area-Integrated Information Dissemination Division, St. Paul, United States, (14)U.S. Geological Survey, MD-DE-DC Water Science Center, Baltimore, United States
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