Exploring the Benefits of Machine Learning: Improving Stormflow Predictions using Long Short-Term Memory Networks
Exploring the Benefits of Machine Learning: Improving Stormflow Predictions using Long Short-Term Memory Networks
Jessica L. Garrett, Lynker, Boulder, United States, Luciana L. Kindl da Cunha, NOAA Affiliate, WEST Consultants, Boulder, United States, Trey Flowers, NOAA Office of Water Prediction, National Water Center, Tuscaloosa, United States, Nels J Frazier, NOAA Affiliate, Lynker, Tuscaloosa, United States, Ahmad Jan, Oak Ridge National Laboratory, Watershed Systems Modeling Group, Environmental Sciences Division, Oak Ridge, United States, Keith Steven Jennings, University of Nevada, Reno, Reno, United States, J. Michael Johnson, NOAA Affiliate, Lynker, Boulder, United States, Fred L. Ogden, NOAA/NWS Office of Water Prediction, National Water Center, Tuscaloosa, United States, Scott Dale Peckham, University of Colorado Boulder, INSTAAR, Boulder, United States and Jonathan M Frame, California State University Monterey Bay, Seaside, CA, United States; University of Alabama, Tuscaloosa, United States
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