H32B:
Applications of Machine Learning in Hydrology II

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Session ID#: 34827

Session Description:
An infusion of large datasets is transforming hydrologic sciences. Hydrologic data comes from satellites in space, meters in streams, wells in the subsurface, and many other sources. Unsupervised machine learning methods are able to extract signals from these datasets that are meaningful and difficult for humans to discern. Supervised machine learning methods can be trained on big datasets coming out of high-fidelity, computationally-intensive computer models to create fast and accurate reduced order models. This session seeks to bring together hydrologists who are applying machine learning methods to improve our understanding of hydrologic systems and enable modeling techniques outside of traditional numerical models.
Primary Convener:  Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, United States
Convener:  Dr. Velimir monty V Vesselinov, Los Alamos National Laboratory, Computational Earth Sciences (EES-16), Los Alamos, NM, United States
Chairs:  Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, United States and Dr. Velimir monty V Vesselinov, Los Alamos National Laboratory, Los Alamos, United States
OSPA Liaison:  Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, United States
Index Terms:

1894 Instruments and techniques: modeling [HYDROLOGY]
1914 Data mining [INFORMATICS]
1916 Data and information discovery [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

Short-range quantitative precipitation forecasting using Deep Learning approaches (213593)
Ata Akbari Asanjan1, Tiantian Yang2, Xiaogang Gao3, Kuo-lin Hsu4 and Soroosh Sorooshian4, (1)University of California Irvine, Irvine, CA, United States, (2)Deltares USA / University of California Irvine, Silver Spring, MD, United States, (3)Univ California Irvine, Irvine, CA, United States, (4)UC Irvine, Irvine, CA, United States
The Use of Convolutional Neural Network in Relating Precipitation to Circulation (213847)
Baoxiang Pan1, Kuo-lin Hsu2, Amir AghaKouchak3 and Soroosh Sorooshian2, (1)University of California Irvine, Irvine, CA, United States, (2)UC Irvine, Irvine, CA, United States, (3)California Department of Water Resources, Irvine, CA, United States
Regional early flood warning system: design and implementation (283946)
Li-Chiu Chang1, Shun-Nien Yang1, Chun-Ling Kuo2 and Yi-Fung Wang2, (1)Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan, (2)Water Resources Agency, Taipei, Taiwan
Building regional early flood warning systems by AI techniques (263534)
Fi-John Chang, National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan, Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan and Mohd Zaki bin Mat Amin, National Hydraulic Research Institute of Malaysia, Research Centre for Water Resources and Climate Change, kuala Lumpur, Malaysia
Boosting flood warning schemes with fast emulator of detailed hydrodynamic models (210432)
Vasilis Bellos1, Juan Pablo Carbajal2 and Joao Paulo Leitao2, (1)National Technical University of Athens (NTUA), Athens, Greece, (2)EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
Data Fusion of Gridded Snow Products Enhanced with Terrain Covariates and a Simple Snow Model (298737)
Andrew M Snauffer, University of British Columbia, Vancouver, BC, Canada, William W Hsieh, Univ British Columbia, Vancouver, BC, Canada and Alex J. Cannon, Environment and Climate Change Canada, Climate Data and Analysis Section, Climate Research Division, Victoria, BC, Canada
Using Unsupervised Learning to Unlock the Potential of Hydrologic Similarity (279553)
Nathaniel W. Chaney, Duke University, Durham, United States and Andrew James Newman, NSF National Center for Atmospheric Research, Boulder, United States

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