H31F:
Uncertainty Quantification, Stochastic Inversion, and Machine Learning Methods in Water and Environmental Management II Posters

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Session ID#: 13319

Session Description:
Climate variability and change affect the occurrence of extreme hydrological events. Extreme weather and climate events pose a serious threat to water and environmental resources management. Recent developments in uncertainty quantification and machine learning have enabled the utilization of big in situ and remote sensing data sets to improve hydroenvironmental modeling and water resources management. Presentations on advanced computational methods, machine learning (e.g. artificial neural networks, fuzzy systems, and evolutionary computation), analysis and processing of big and/or complex data (remote sensing information, feature extraction, nonlinear time series analysis, environmental data fusion, inversion and assimilation), and uncertainty analysis in water and environmental management are welcome. This session aims to provide a forum to share the current state of the art in computational methods, stochastic inversion and machine learning and to demonstrate these techniques with applications in the water resources management, hydrology, ecology, atmospheric, energy, and environmental sciences.
Primary Convener:  Kuo-lin Hsu, University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States
Conveners:  Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, United States and Xiao Chen, Lawrence Livermore National Laboratory, Livermore, CA, United States
Chairs:  Xiao Chen, Lawrence Livermore National Laboratory, Livermore, CA, United States, Kuo-lin Hsu, University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States and Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, United States
OSPA Liaison:  Kuo-lin Hsu, University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States

Cross-Listed:
  • PA - Public Affairs
Index Terms:

1805 Computational hydrology [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
1906 Computational models, algorithms [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

A Probabilistic Collocation Based Iterative Kalman Filter for Landfill Data Assimilation (124923)
Qiang Zheng1, Lingzao Zeng1 and Laosheng Wu2, (1)Zhejiang University, College of Environmental and Resource Sciences, Hangzhou, China, (2)Zhejiang University, Hangzhou, China
A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification (Invited) (126603)
Charles Talbot, Xiao Chen, Charles H Tong and Charanraj Thimmisetty, Lawrence Livermore National Laboratory, Livermore, CA, United States
An efficient adaptive sampling strategy for global surrogate modeling with applications in multiphase flow simulation (127527)
Shaoxing Mo1, Dan Lu2, Xiaoqing Shi1, Guannan Zhang3, Ming Ye4, Jianfeng Wu1 and Jichun Wu1, (1)Nanjing University, Nanjing, China, (2)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (3)Oak Ridge National Laboratory, Oak Ridge, United States, (4)Florida State University, Earth, Ocean, and Atmospheric Science, Tallahassee, United States
An Integrated Probabilistic-Fuzzy Assessment of Uncertainty Associated with Human Health Risk to MSW Landfill Leachate Contamination (135354)
Harshit Mishra, Indian Institute of Technology Bombay, Mumbai, India, Subhankar Karmakar, Indian Institute of Technology Bombay, Interdisciplinary Programme in Climate Studies, Mumbai, India and Rakesh Kumar, Nalanda University, School of Ecology & Environment Studies, Rajgir, India
Stage-discharge relationship in tidal channels (135635)
William Sheppard Kearney, Boston University, Department of Earth and Environment, Boston, MA, United States, Giulio Mariotti, Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, LA, United States, Linda Deegan, Marine Biological Laboratory, Ecosystems Center, Woods Hole, MA, United States and Sergio Fagherazzi, Boston University, Department of Earth and Environment, Boston, United States
Exploring the added value of machine learning methods in predicting flow duration curves: a comparative analysis for ungauged catchments (136609)
Elcin Kentel, Middle East Technical University, Department of Civil Engineering, Ankara, Turkey and Nilay Dogulu, Middle East Technical University, Ankara, Turkey
Applying Machine Learning to Climate Forcing of Streamflow: Regression Ensembles Illuminate Local Influences on Watershed Responses to ENSO. (139022)
Joshua S Rice, North Carolina State University Raleigh, Forestry and Environmental Resources, Raleigh, NC, United States and Ryan E Emanuel, North Carolina State University, Department of Forestry and Environmental Resources, Raleigh, NC, United States
Evaluation of data worth of hydraulic head and temperature in estimating hydraulic conductivities (143378)
Lei Ju1, Jiangjiang Zhang1 and Lingzao Zeng2, (1)Zhejiang University, Hangzhou, China, (2)Zhejiang University, College of Environmental and Resource Sciences, Hangzhou, China
Efficient and Robust Classification of Seismic Data through Dimensionality Reduction (187209)
Gowri Srinivasan, Kyle S. Hickmann and Jeffrey Hyman, Los Alamos National Laboratory, Los Alamos, NM, United States
Model Reduction Using Principal Component Analysis and Markov Chain Monte Carlo for Hydrogeological Inverse Problems (158482)
Yue Zhao1, Saubhagya Rathore2, Jinsong Chen3, Gary Michael Hoversten4 and Jian Luo1, (1)Georgia Institute of Technology Main Campus, Atlanta, GA, United States, (2)Georgia Institute of Technology Main Campus, School of Civil and Environmental Engineering, Atlanta, GA, United States, (3)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (4)Chevron Corporation Houston, Geophysical R&D, Houston, TX, United States
Identifying Attributes of CO2 Leakage Zones in Shallow Aquifers Using a Parametric Level Set Method (159201)
Akand Islam1, Alexander Y Sun2 and Mary Wheeler1, (1)University of Texas at Austin, Austin, TX, United States, (2)University of Texas at Austin, Bureau of Economic Geology, Austin, United States
History Matching for Fractured Reservoirs using Mimetic Finite Differences and Ensemble Kalman Filter (168425)
Jing Ping1, Omar Al-Hinai1, Sanjay Srinivasan2, Mary Wheeler1 and Baehyun Min1, (1)University of Texas at Austin, Austin, TX, United States, (2)Pennsylvania State University Main Campus, Department of Energy and Mineral Engineering, University Park, PA, United States
Explore the Spatial-Temporal Interrelation between Groundwater and Surface Water by Self-Organizing Map (168608)
I-Ting Chen, Tamkang University, Dept. of Water Resources and Environmental Engineering, Taipei, Taiwan, Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan and Fi-John Chang, NTU National Taiwan University, Bio-environmental Systems Engineering, Taipei, Taiwan
Coupling Precipitation with Groundwater Level Using Two Self-Organizing Maps (169558)
Jia-Wei Lai1, Chien-Wei Huang1, Su-Ting Cheng2, Li-Chiu Chang3 and Fi-John Chang4, (1)National Taiwan University, Taipei, Taiwan, (2)National Taiwan University, School of Forestry & Resource Conservation, Taipei, Taiwan, (3)Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan, (4)National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan
Improving predictions of hydrological low-flow indices in ungaged basins using machine learning (174253)
Scott C. Worland, USGS Tennessee Water Science Center, Nashville, TN, United States, William Hastings Farmer, U.S Geological Survey, Denver, United States and Julie E. Kiang, US Geological Survey, Reston, United States
Identifying Aquifer Heterogeneities using the Level Set Method (180184)
Zhiming Lu, Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States, Dr. Velimir monty V Vesselinov, Los Alamos National Laboratory, Los Alamos, NM, United States and Hongzhuan Lei, Florida State University, Scientific Computing, Tallahassee, FL, United States
Reduced-order modeling with sparse polynomial chaos expansion and dimension reduction for evaluating the impact of CO2 and brine leakage on groundwater (182196)
Yaning Liu1, Liange Zheng2 and George Shu Heng Pau1, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Energy Geosciences Division, Berkeley, United States
Leveraging Advanced Computational Algorithms and Satellite Remote Sensing Products for Flood Forecasting (182515)
Fernando Aristizabal, NOAA Affiliate, Lynker, Tuscaloosa, United States, Jasmeet Judge, University of Florida, Center for Remote Sensing, Agricultural and Biological Engineering, Ft Walton Beach, FL, United States and Anand Rangarajan, University of Florida, Department of Computer & Information Science & Engineering, Ft Walton Beach, FL, United States
Extracting 5m Shorelines From Multi-Temporal Images (186817)
Amit Kapadia, Kelsey A Jordahl and Joe D Kington IV, Planet Labs, San Francisco, CA, United States
Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network (187802)
Usman T Khan, York University, Civil Engineering, Toronto, ON, Canada

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