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ms-pgdl-wrr

Code for the PGDL manuscript for the WRR Special Issue on "Big Data & Machine Learning in Water Sciences: Recent Progress and Their Use in Advancing Science"

Process-guided deep learning predictions of lake water temperature

Jordan S. Read1, Xiaowei Jia2, Jared Willard2, Alison P. Appling1, Jacob A. Zwart1, Samantha K. Oliver1, Anuj Karpatne3, Gretchen J.A. Hansen4, Paul C. Hanson5, William Watkins1, Michael Steinbach2, Vipin Kumar2

1U.S. Geological Survey 2Department of Computer Science and Engineering, University of Minnesota 3Department of Computer Science, Virginia Tech 4Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota 5University of Wisconsin-Madison, Center for Limnology

folders and names corresponding to figure 1, 2, 3, and 4 are actually aligned with manuscript figure numbers 2, 3, 4, and 5 (the three "results" figures, as figure 1 in the manuscript is a model schematic).

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  • Python 67.4%
  • R 32.6%