H33C:
Applications of Machine Learning in Hydrology III Posters

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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:  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:

Improving Simulations of Extreme Flows by Coupling a Physically-based Hydrologic Model with a Machine Learning Model (206164)
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
Moving Stormwater Infrastructure from Real-time Control to Smart Systems (214455)
Bridget Wadzuk1, Seth Bryant2, Conor Lewellyn2, Gerald Zaremba2 and Robert Traver2, (1)Villanova University, Civil and Environmental Engineering, Villanova, United States, (2)Villanova University, Villanova, PA, United States
Integrated Application of Random Forest and Artificial Neural Network Algorithms to Predict Viral Contamination in Coastal Waters (219233)
Shima Shamkhali Chenar, Louisiana State University, Baton Rouge, LA, United States and Zhiqiang Deng, Louisiana State University, Department of Civil and Environmental Engineering, Baton Rouge, LA, United States
Research of Water Level Prediction for a Continuous Flood due to Typhoons Based on a Machine Learning Method (222430)
Makoto Nakatsugawa1, Yosuke Kobayashi2, Ryota Okazaki3 and Yoko Taniguchi2, (1)Graduate School of Engineering, Muroran Institute of Technology, Muroran, Japan, (2)Muroran Institute of Technology, Muroran, Japan, (3)Muroran Institute of Tecnology, Muroran, Japan
Temporal change analysis of Lakes Azuei and Enriquillo behavior based on trend and change point approaches (233821)
Mahrokh Moknatian, CUNY Hunter College, New York, NY, United States and Michael Piasecki, CUNY City College of New York, New York, NY, United States
Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality (246157)
Scott Alan Bradford1, Jing Liang2, Wenzhe Li3, Taichi Murata4 and Jiri Simunek2, (1)USDA-ARS, Sustainable Agricultural Water Systems, Davis, 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
Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh (249910)
Zhen Tan1,2, Qiang Yang3, Chunmiao Zheng1,4 and Yan Zheng2,5, (1)Peking University, Institute of Water Sciences, Beijing, China, (2)Southern University of Science and Technology, School of Environmental Science and Engineering, Shenzhen, China, (3)Lamont -Doherty Earth Observatory of Columbia University, Palisades, NY, United States, (4)Southern University of Science and Technology, School of Env. Sci. & Engr., Shenzhen, China, (5)Columbia University of New York, Lamont-Doherty Earth Observatory, Palisades, United States
Seasonal streamflow forecast with machine learning and teleconnection indices in the context non-stationary climate (258547)
Didier Haguma and Robert Leconte, University of Sherbrooke, Civil and Building Engineering, Sherbrooke, QC, Canada
Application of Spatial Neural Network Model for Optimal Operation of Urban Drainage System (271755)
Beom-Jin KIM, Kyungpook National University, Daegu, South Korea, Jae Yeong Lee, Kyungpook National University, Daegu, Korea, Republic of (South), Hyun Il KIM, Kyungpook National University, civil, Daegu, South Korea, Ah Long Son, National Disaster Management Research Institute, Ulsan, South Korea and Han Kun-Yun, Kyungpook National University, Professor, Daegu, South Korea
Developing a Storm Tracking Algorithm using Satellite Image Segmentation and Optical Flow Techniques  (294608)
Negin Hayatbini1, Kuo-lin Hsu2, Tiantian Yang3 and Soroosh Sorooshian2, (1)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (2)UC Irvine, Irvine, CA, United States, (3)University of California Irvine, Irvine, CA, United States
Validating the Use of Deep Learning Neural Networks for Correction of Large Hydrometric Datasets (299695)
Nels Frazier1, Fred L Ogden2, Jason A. Regina1 and Yanyan Cheng3, (1)University of Wyoming, Laramie, WY, United States, (2)NOAA Affiliate, University Corporation for Atmospheric Research, Office of Water Prediction, National Water Center, Tuscaloosa, AL, United States, (3)Pacific Northwest National Laboratory, Richland, United States
A Data-driven Approach for Forecasting Next-day River Discharge (225515)
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

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