H24D:
Uncertainty Quantification, Stochastic Inversion, and Machine Learning Methods in Water and Environmental Management I

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Session ID#: 17293

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:  Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, 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:

Inverse Modeling Using Markov Chain Monte Carlo Aided by Adaptive Stochastic Collocation Method with Transformation (Invited) (124707)
Dongxiao Zhang1 and Qinzhuo Liao1,2, (1)Peking University, Department of Energy and Resources Engineering, Beijing, China, (2)Stanford University, Stanford, CA, United States
Surrogate model based iterative ensemble smoother for subsurface flow data assimilation (Invited) (124710)
Dongxiao Zhang, Haibin Chang and Qinzhuo Liao, Peking University, Department of Energy and Resources Engineering, Beijing, China
An Efficient Method Coupling Kernel Principal Component Analysis with Adjoint-Based Optimal Control and Its Goal-Oriented Extensions (Invited) (126551)
Charles Talbot, Charles H Tong, Xiao Chen and Charanraj Thimmisetty, Lawrence Livermore National Laboratory, Livermore, CA, United States
Solving stochastic inverse elasticity problems via gradient-enhanced kernel PCA (143000)
Wenju Zhao1, Xiao Chen2, Charles H Tong2, Joshua A. White3 and Charanraj Thimmisetty2, (1)Florida State University, Tallahassee, FL, United States, (2)Lawrence Livermore National Laboratory, Livermore, CA, United States, (3)Lawrence Livermore National Laboratory, Livermore, United States
Selection of Representative Models for Decision Making and Optimization under Geological Uncertainty (152640)
Mehrdad Gharib Shirangi, Stanford University, Stanford, CA, United States and Louis J Durlofsky, Stanford University, Department of Energy Science and Engineering, Stanford, United States
Stochastic generation of daily rainfall events based on rainfall pattern classification and Copula-based rainfall characteristics simulation (177714)
Yue-ping Xu, Zhejiang University, Institute of Water Science and Engineering, Hangzhou, China and Chao Gao, Zhejiang University, Hangzhou, China
Evaluating the Implications of Climate Phenomenon Indices in Supporting Reservoir Operation Using the Artificial Neural Network and Decision-Tree Methods: A Case Study on Trinity Lake in Northern California (118666)
Tiantian Yang1, Ata Akbari Asanjan1, Xiaogang Gao1 and Soroosh Sorooshian2, (1)University of California Irvine, Irvine, CA, United States, (2)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States
Multi-objective evolutionary optimization for the joint operation of reservoirs of water supply under water-food-energy nexus management (168511)
Tinn-Shuan Uen1, Wen-Ping Tsai1, Fi-John Chang2 and Angela Huang2, (1)National Taiwan University, Bioenvironmental Systems Engineering, Taipei, Taiwan, (2)National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan

See more of: Hydrology