H191
Machine Learning in Hydrologic Modeling II

Tuesday, 15 December 2020: 20:30-21:30
Virtual
Primary Convener:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States
Conveners:  Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, Hoshin Gupta, Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States and Frederik Kratzert, Johannes Kepler University, Institute for Machine Learning, Linz, Austria
Primary Liaison:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States
Chairs:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States and Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States
OSPA Liaison:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States
20:30
H191-01
A coupled approach to incorporating deep learning into process-based hydrologic modeling (675239)
Andrew Bennett, University of Washington Seattle Campus, Seattle, WA, United States and Bart Nijssen, University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, WA, United States
20:34
H191-02
Getting the most out of satellite-based precipitation data for forecasting river floods with deep neural networks (676324)
Efrat Morin1,2, Cenk Gazen2, Zach Moshe3, Ofir Reich3, Asher Metzger3, Guy Shalev3, Gregory Begelman3, Shreya Agrawal2, Jason Hickey2 and Sella Nevo3, (1)The Hebrew University of Jerusalem, The Fredy and Nadine Herrmann Institute of Earth Sciences, Jerusalem, Israel, (2)Google, Research, Mountain View, United States, (3)Google, Research, Tel Aviv, Israel
20:38
H191-03
Regional hydrological modelling with deep convolutional-recurrent neural networks: A case study in Western Canada (678145)
Sam Anderson, University of British Columbia, Department of Earth, Ocean, and Atmospheric Sciences, Vancouver, BC, Canada and Valentina Radic, University of British Columbia, Department of Earth, Ocean and Atmospheric Sciences, Vancouver, BC, Canada
20:42
H191-04
Big Data for Specific Places in Hydrologic Modeling (756301)
Alden Keefe Sampson, Eliza Hale and David Lambl, Natel Energy Inc, Upstream Tech, Alameda, CA, United States
20:46
H191-05
Hypothesis Testing using Long Short-Term Memory Networks Applied to Large Pixel-Scale Datasets (720333)
Yuan-Heng Wang1, Hoshin Gupta2, Grey Stephen Nearing3, Xubin Zeng1 and Guo-Yue Niu4, (1)University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (2)Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States, (3)Natel Energy Inc, Upstream Tech, Alameda, CA, United States, (4)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States
20:50
H191-06
Opening the black box of LSTM models using XAI (762234)
Xingyuan Chen1, Peishi Jiang1, Justine E.C. Missik2, Zhongming Gao2, Brittany Verbeke1 and Heping Liu3, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Washington State University, Pullman, WA, United States, (3)Washington State University, Civil and Environmental Engineering, Pullman, WA, United States
20:54
Discussion
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