Primary Convener: Amir Hossein Mazrooei, National Center for Atmospheric Research, Boulder, CO, United States
Conveners: Pierre Gentine, Columbia University, Earth and Environmental Engineering, New York, NY, United States and Andrew W Wood, National Center for Atmospheric Research, Boulder, CO, United States
Primary Liaison: Amir Hossein Mazrooei, National Center for Atmospheric Research, Boulder, CO, United States
Chairs: Pierre Gentine, Columbia University, Earth and Environmental Engineering, New York, NY, United States and Andrew W Wood, National Center for Atmospheric Research, Boulder, CO, United States
OSPA Liaison: Pierre Gentine, Columbia University, Earth and Environmental Engineering, New York, NY, United States
19:00
What is the role of hydrological science in the age of machine learning? (Invited) (665894)
Grey Stephen Nearing1, Frederik Kratzert2, Alden Keefe Sampson3, Craig Pelissier4, Daniel Klotz2, Cristina Prieto5, Jonathan M Frame6 and Hoshin Gupta7, (1)Natel Energy Inc, Upstream Tech, Alameda, CA, United States, (2)Johannes Kepler University, Institute for Machine Learning, Linz, Austria, (3)Upstream Tech, Alameda, CA, United States, (4)NASA Goddard Space Flight Center, Computational and Information Science & Technology Office, Greenbelt, MD, United States, (5)University of Cantabria, Environmental Hydraulics Institute "IH Cantabria", Santander, Spain, (6)California State University Monterey Bay, Seaside, CA, United States, (7)Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States
19:04
LSTM-Based Rainfall–Runoff Modeling at Arbitrary Time Scales (736425)
Martin Gauch1, Frederik Kratzert2, Daniel Klotz2, Grey Stephen Nearing3 and Jimmy Lin1, (1)University of Waterloo, David R. Cheriton School of Computer Science, Waterloo, ON, Canada, (2)Johannes Kepler University, Institute for Machine Learning, Linz, Austria, (3)Natel Energy Inc, Upstream Tech, Alameda, CA, United States
19:12
The new US Department of Agriculture snowmelt runoff and water supply forecast model for the American West: leveraging multi-model ensembles, evolutionary computing, and automated, theory-guided, interpretable artificial intelligence (673126)
Sean William Fleming1,2, David C. Garen3, Angus Graham Goodbody4, Cara McCarthy5 and Lexi Landers3, (1)Oregon State University, Corvallis, OR, United States, (2)Natural Resources Conservation Service Portland, US Department of Agriculture, Portland, OR, United States, (3)Natural Resources Conservation Service Portland, US Department of Agriculture, Portland, United States, (4)Natural Resource Conservation Service, Portland, United States, (5)Natural Resources Conservation Service, Portland, United States
19:16
Machine Learning for Flood Peak Prediction in Ungauged Basins (749892)
Zimeena Rasheed, Florida Institute of Technology, Mechanical and Civil Engineering, Melbourne, FL, United States, Akshay Aravamudan, Florida Institute of Technology, Computer Engineering and Sciences, Melbourne, FL, United States, Georgios Anagnostopoulos, Florida Institute of Technology, Computer Engineering and Sciences, Melbourne, United States, Ali Gorji Sefidmazgi, Florida Institute of Technology, Department of Mechanical and Civil Engineering, Melbourne, FL, United States and Efthymios I Nikolopoulos, Florida Institute of Technology, Mechanical and Civil Engineering, Melbourne, United States
