H224
Machine Learning in Hydrologic Modeling I

Thursday, 17 December 2020: 05:30-06:30
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:  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
OSPA Liaison:  Hoshin Gupta, Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States
05:30
H224-01
Automatic Estimation of Parameter Transfer Functions for Distributed Hydrological Models - Function Space Optimization Applied on the mHM Model (720665)
Moritz Feigl1, Robert Schweppe2, Stephan Thober3, Mathew Herrnegger1, Luis Samaniego3 and Karsten Schulz1, (1)BOKU University of Natural Resources and Life Sciences, Institute for Hydrology and Water Management, Vienna, Austria, (2)Helmholtz Centre for Environmental Research UFZ Leipzig, Computational Hydrosystems, Leipzig, Germany, (3)Helmholtz Centre for Environmental Research - UFZ, Computational Hydrosystems, Leipzig, Germany
05:34
H224-02
Process-Guided Deep Learning for Water Temperature Prediction (Invited) (764607)
Alison Appling1, Xiaowei Jia2, Jared Willard3, Samantha Oliver4, Jeffrey Michael Sadler4, Jacob A Zwart5, Jordan Stuart Read4 and Vipin Kumar6, (1)USGS, State College, PA, United States, (2)University of Pittsburgh, Pittsburgh, PA, United States, (3)University of Minnesota, Minneapolis, United States, (4)USGS, Middleton, WI, United States, (5)USGS Integrated Information Dissemination Division, Data Science Branch, Middleton, WI, United States, (6)University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, MN, United States
05:42
H224-03
Equip Deep Learning with Physical Insights: Towards a Symbiotic Integration for Hydrologic Modeling (668888)
Shijie Jiang1,2 and Yi Zheng1, (1)Southern University of Science and Technology, School of Environmental Science and Engineering, Shenzhen, China, (2)National University of Singapore, Department of Civil and Environmental Engineering, Singapore, Singapore
05:46
H224-04
Routing flood waves through the river network utilizing physics-guided machine learning and the Muskingum-Cunge Method (771067)
Tadd Bindas1, Chaopeng Shen1 and Yuchen Bian2, (1)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, (2)Baidu Research, USA, Sunnyvale, CA, United States
05:50
H224-05
Capturing continental-scale dissolved oxygen patterns using deep learning and big data (712773)
Wei Zhi1, Dapeng Feng1, Wen-Ping Tsai1, Gary Sterle2, Adrian Adam Harpold2, Chaopeng Shen1 and Li Li1, (1)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, (2)University of Nevada Reno, Department of Natural Resources and Environmental Science, Reno, NV, United States
05:54
Discussion
See more of: Hydrology
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