H022:
Applications of machine learning in hydrology





Session ID#: 24633

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:  Velimir V Vesselinov, Los Alamos National Laboratory, Computational Earth Sciences (EES-16), Los Alamos, NM, United States

Abstracts Submitted to this Session:

Khaled Mohammed, Akm Saiful Islam, Md Jamal Uddin Khan and Mohan Kumar Das, Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh
Boyko Dodov, AIR Worldwide Boston, Boston, MA, United States
Vasilis Bellos1, Juan Pablo Carbajal2 and Joao Paulo Leitao2, (1)National Technical University of Athens (NTUA), Marousi Athens, Greece, (2)EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
Bridget Wadzuk, Seth Bryant, Conor Lewellyn, Gerald Zaremba and Robert Traver, Villanova University, Villanova, PA, United States
Makoto Nakatsugawa1, Yosuke Kobayashi1, Ryota Okazaki2 and Yoko Taniguchi1, (1)Muroran Institute of Technology, Muroran, Japan, (2)Muroran Institute of Tecnology, Muroran, Japan
Kingsley Abrokwah and Andrew M O'Reilly, University of Mississippi Main Campus, University, MS, United States
Hatim Osman Sharif, University of Texas at San Antonio, Department of Civil and Environmental Engineering, San Antonio, TX, United States and Khondoker S Billah, University of Texas, San Antonio, United States
Ankit Deshmukh1, Ashok Samal2 and Riddhi Singh1, (1)Indian Institute of Technology Hyderabad, Civil Engineering, Hydearbad, India, (2)University of Nebraska-Lincoln, Computer Science Engineering, Lincoln, NE, United States
Francesca Boso, Stanford University, Stanford, CA, United States and Daniel M Tartakovsky, Stanford University, Energy Resources Engineering, Stanford, CA, United States
Mahrokh Moknatian and Michael Piasecki, CUNY City College of New York, New York, NY, United States
Joeseph Smith, Organization Not Listed, Ann Arbor, MI, United States and Andrew Gronewold, NOAA-Great Lakes Environmental Research Laboratory, Ann Arbor, MI, United States
Sarah J Praskievicz and Cehong Luo, University of Alabama, Geography, Tuscaloosa, AL, United States
Bidhyananda Yadav, University of Florida, Civil and Coastal Engineering, Gainesville, FL, United States and Kirk Hatfield, University of Florida, Gainesville, United States
Scott Alan Bradford1, Jing Liang2, Wenzhe Li3, Taichi Murata4 and Jiri Simunek2, (1)USDA, ARS, US Salinity Laboratory, Riverside, CA, United States, (2)Department of Environmental Sciences, University of California Riverside, Riverside, CA, United States, (3)University of Southern California, Computer Science, Los Angeles, CA, United States, (4)University of California, Environmental Sciences, Riverside, CA, United States
Zhen Tan1,2, Qiang Yang3, Chunmiao Zheng1,2 and Yan Zheng2,4, (1)Peking University, Institute of Water Sciences, Beijing, China, (2)Southern University of Science and Technology, School of Env. Sci. & Engr., Shenzhen, China, (3)Lamont -Doherty Earth Observatory, Palisades, NY, United States, (4)Columbia University of New York, Lamont-Doherty Earth Observatory, Palisades, NY, United States
Maruti Kumar Mudunuru, Satish Karra and Velimir V Vesselinov, Los Alamos National Laboratory, Los Alamos, NM, United States
Didier Haguma and Robert Leconte, University of Sherbrooke, Sherbrooke, QC, Canada
Beom-Jin KIM1, Jae Yeong Lee1, Hyun Il KIM2, Ah Long Son3 and Kun Yeun Han4, (1)Kyungpook National University, Daegu, Korea, Republic of (South), (2)Kyungpook National University, Daegu, South Korea, (3)National Disaster Management Research Institute, Ulsan, South Korea, (4)Kyungpook National University, Professor, Daegu, South Korea
Katarina Doctor and Jefferson M Byers, Naval Research Laboratory, Washington, DC, United States
Jefferson M Byers and Katarina Doctor, Naval Research Laboratory, Washington, DC, United States
Jeffrey Michael Sadler1, Jonathan L Goodall1, Mohamed M Morsy1 and Kyle Spencer2, (1)University of Virginia, Charlottesville, VA, United States, (2)City of Norfolk, Norfolk, VA, United States
Dongfeng Li, University of Texas at Arlington, Arlington, TX, United States and Nick Z. Fang, Univ of TX-Arlington-Civil Eng, Arlington, TX, United States
Velimir V Vesselinov, Los Alamos National Laboratory, Los Alamos, NM, United States
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
Nels Frazier1, Fred L Ogden2, Jason A. Regina1 and Yanyan Cheng1, (1)University of Wyoming, Laramie, WY, United States, (2)Univ. of Wyoming and UCAR, Laramie, WY, United States

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