H21J:
Big Data and Machine Learning in Hydrology and Subsurface Flow and Transport III Posters


Submit an Abstract to this Session


Session ID#: 54197

Session Description:
We are witnessing tremendous advances in analysis of massive data streams, ultra-fast computing resources, physics-informed machine learning as well as significant increase in instrumentation density and quality. These advances present opportunities for novel methods to aid in both scientific discovery, data discovery, and predictive modeling. This session invites contributions describing applications of machine learning methods and “big” and “small” data problems for a) enhance decision making, b) producing new datasets, d) gaining new physical insights in hydrologic processes, e) investigating connections between water and other physical/human systems. The target applications include water resources management, subsurface reactive transport, CO2 sequestration and other related disciplines.
Primary Convener:  Alexandre M Tartakovsky, University of Illinois at Urbana Champaign, Civil and Environmental Engineering, Champaign, United States
Conveners:  Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, Dr. Grey Stephen Nearing, PHD, Upstream Tech, Tuscaloosa, United States and Gowri Srinivasan, Los Alamos National Laboratory, Los Alamos, NM, United States
Primary Liaison:  Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States
Chairs:  Alexandre M Tartakovsky, University of Illinois at Urbana Champaign, Civil and Environmental Engineering, Champaign, United States, Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, Dr. Grey Stephen Nearing, PHD, Upstream Tech, Tuscaloosa, United States and Gowri Srinivasan, Los Alamos National Laboratory, Los Alamos, NM, United States
OSPA Liaison:  Gowri Srinivasan, Los Alamos National Laboratory, Los Alamos, NM, United States

Cross-Listed:
  • IN - Earth and Space Science Informatics
Index Terms:

1869 Stochastic hydrology [HYDROLOGY]
1895 Instruments and techniques: monitoring [HYDROLOGY]
1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

Simulation Study of Canada Zama Reservoir Using TDRM(AI) (350804)
Taehun Lee1, Wonsuk Lee2, Kyungbook Lee2 and Hyunsuk Lee1, (1)KIGAM Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea, (2)KIGAM Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea, Republic of (South)
Predicting US Drought Monitor Drought Categories with multiple land surface models and machine learning (374366)
Pouyan Hatami, Michigan State University, East Lansing, MI, United States, Lifeng Luo, Michigan State University, Department of Geography, Environment, and Spatial Sciences, East Lansing, MI, United States, Lisi Pei, Georgia Institute of Technology, School of Civil & Environmental Engineering, Atlanta, United States, Xi Liu, Michigan State University, Computer Science and Engineering, East Lansing, United States, Tyler Wilson, Michigan State University, Computer Science and Engineering, East Lansing, MI, United States and Pang-Ning Tan, Michigan State University, Department of Computer Science and Engineering, East Lansing, United States
Research on prediction of Water Level of Low Frequency Flood on Rivers in Japan Using the Random Forest Method (388827)
Riko Sakamoto1, Makoto Nakatsugawa2 and Yosuke Kobayashi1, (1)Muroran Institute of Technology, Muroran, Japan, (2)Graduate School of Engineering, Muroran Institute of Technology, Muroran, Japan
GEONEX: CloudCNN 2.0 - A Deep Neural Network for Real-time Cloud Type Classification and Segmentation from Geostationary Data (400940)
Subodh Kalia1, Sangram Ganguly2, Shuang Li1, Vesta Afzali Gorooh3, Andrew Michaelis1, Gayaka Shreekant4 and Ramakrishna R Nemani1, (1)NASA Ames Research Center, Moffett Field, CA, United States, (2)Rhombus Power, Palo Alto, United States, (3)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (4)BAER Institute, Mountainview, CA, United States
Reservoir Operation based on Machine Learning combined with Daily Inflow Forecasting by a Distributed Hydrological Model (400989)
Shuyu Yang, Tsinghua University, Department of Hydraulic Engineering, Beijing, China and Dawen Yang, Tsinghua University, State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Beijing, China
Scalable Inverse Estimation with Variational Inference (409545)
Christopher Krapu, Duke University, Civil and Environmental Engineering, Durham, United States and Mark E Borsuk, Duke University, Department of Civil and Environmental Engineering, Durham, United States
Detection of shifting flow regimes for watersheds in western North America using Deep Learning techniques (411247)
Weigang Tang, McMaster University, Hamilton, ON, Canada and Sean Kevin Carey, McMaster University, School of Earth, Environment and Society, Hamilton, ON, Canada
Toward Forecasting Groundwater Table in Flood Prone Coastal Cities Using Long Short-term Memory and Recurrent Neural Networks (433082)
Benjamin Donald Bowes1, Jonathan L Goodall2, Jeffrey Michael Sadler3, Mohamed M Morsy4 and Madhur Behl1, (1)University of Virginia, Charlottesville, VA, United States, (2)University of Virginia, Department of Civil and Environmental Engineering, Charlottesville, VA, United States, (3)Oaklahoma State University, Biosystems & Agricultural Engineering, Stillwater, United States, (4)University of Virginia Main Campus, Charlottesville, VA, United States
Application of Spectrum Analysis and Decision Tree to Aquifer Classification – A case study of Zhuoshui River Alluvial Fan (433502)
Yi-Hao Chang1, Chih-Hsiang Hsu1, Liang Cheng Chang1, Yu-Wen Chen1,2 and Yun-Shuen Wang3, (1)National Chiao Tung University, Hsinchu, Taiwan, (2)University of California Los Angeles, Los Angeles, CA, United States, (3)Central Geological Survey, MOEA, New Taipei City, Taiwan
Deep Learning Model Integration of Remotely Sensed and SWAT-Simulated Regional Soil Moisture (439753)
Kathy Breen, Baylor University, Waco, TX, United States, Scott C James, Baylor University, Geosciences and Mechanical Engineering, Waco, TX, United States and Joseph D White, Baylor University, Department of Biology, Waco, TX, United States
Application of the long short-term memory network for hydrologic time series forecasting (464700)
Taereem Kim, Yonsei University, Seoul, Korea, Republic of (South), Kyungwon Joo, Yonsei University, Seodaemun, Korea, Republic of (South), Sunghun Kim, Missouri University of Science and Technology, Civil, Architectural and Environmental Engineering, Rolla, MO, United States and Jun-Haeng Heo, Yonsei University, School of Civil & Environmental Engineering, Seoul, South Korea
Machine Learning for Quantifying and Reducing Model Uncertainty in Non-Linear Partial Differential Equation Models (425420)
Alexandre M Tartakovsky, University of Illinois at Urbana Champaign, Civil and Environmental Engineering, Champaign, United States and David A Barajas-Solano, Pacific Northwest National Laboratory, Richland, United States
A comparative evaluation of multiple machine learning approaches for short range flood level prediction (426012)
Abdou Khouakhi, Cranfield University, School of Water, Energy and Environment, Cranfield, United Kingdom and Ian Pattison, Loughborough University, School of Architecture, Civil and Building Engineering, Loughborough, United Kingdom
Artificial Intelligence as an Efficient Alternative to the Conventional Hydrological Modelling for Groundwater Forecasting (458102)
Samarth Shah1, Hamidreza Ghasemi Damavandi2, Li Huang2, Dimitrios Stampoulis2, Reepal Shah2, Yushiou Tsai2, John L Sabo3 and Dragan Boscovic4, (1)Arizona State University, FutureH2O, Knowledge Enterprise Development, Tempe, AZ, United States, (2)Arizona State University, Future H2O, Knowledge Enterprise Development, Tempe, AZ, United States, (3)Arizona State University, Future H2O, Knowledge Enterprise Development, Tempe, United States, (4)Arizona State University, CASCADE, Tempe, AZ, United States
Comparative Applications of Data-Driven Models Representing Water Table Fluctuations (362273)
Jina Jeong1, Eungyu Park2 and Jiho Jeong2, (1)Kyungpook National University, Department of Geology, Daegu, South Korea, (2)Kyungpook National University, Department of Geology, Daegu, Korea, Republic of (South)
Contribution and relative importance of input variables in artificial neural network for the groundwater level forecasting (366565)
Sanghoon Lee, Seoul National University, Seoul, Korea, Republic of (South) and Kang Kun Lee, Seoul National University, School of Earth and Environmental Sciences, Seoul, South Korea
Efficacy of Machine Learning Algorithms for Identifying Hotspots of Groundwater Depletion in Intensively Irrigated Agricultural Regions (414301)
Ammara Talib, University of Wisconsin Madison, Department of Atmospheric and Oceanic Sciences, Madison, WI, United States and Ankur R Desai, University of Wisconsin Madison, Atmospheric and Oceanic Sciences, Madison, WI, United States
Investigating the role of soil structure on hydraulic properties using machine learning models and study of soils from long-term conservation agriculture (468121)
Teamrat A Ghezzehei, University of California Merced, Life and Environmental Sciences, Merced, CA, United States, Samuel Negusse Araya, University of California Merced, Merced, CA, United States and Jeffery P Mitchell, UC ANR, Davis, CA, United States
Location and magnitude of preferential flow path formation in carbonate rocks, forward prediction from pre-experimental CT-scan using machine learning (405677)
Florent Brondolo1, Phil Cilli2, Ian B Butler3, Katriona Edlmann3 and Christopher McDermott3, (1)University of Edinburgh, School of GeoSciences, Edinburgh, United Kingdom, (2)University of Edinburgh, School Of Geosciences, Edinburgh, EH9, United Kingdom, (3)University of Edinburgh, School of Geosciences, Edinburgh, United Kingdom
Multivariate Time Series Data Imputation for Groundwater Modeling in the North China Plain (390255)
Hao Jing1,2, Xin He1, Guoliang Cao3 and Chunmiao Zheng1, (1)Southern University of Science and Technology, School of Environmental Science and Engineering, Shenzhen, China, (2)HIT Harbin Institute of Technology, Harbin, China, (3)China Institute of Water Resources and Hydropower Research, Beijing, China
Physics Informed Deep Neural Networks for learning parameters with non-Gaussian non-stationary statistics (458513)
Guzel Tartakovsky, Pacific Northwest National Laboratory, Richland, WA, United States, Alexandre M Tartakovsky, University of Illinois at Urbana Champaign, Civil and Environmental Engineering, Champaign, United States and Paris Perdikaris, university of Pennsylvania, Philadelphia, MA, United States
Prediction of Groundwater Levels for the Extreme Events Using Artificial Neural Networks (463196)
Li-Chiu Chang1, Shun-Nien Yang1 and Ying-Yu Ku2, (1)Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan, (2)Tamkang University, Department of Water Resources and Environmental Engineering, New Taipei, Taiwan
Quantifying machine learning based segmentation for digital rock analyses from the µm to the nm scale (380880)
Matthew Andrew, Carl Zeiss Microscopy Inc., Pleasanton, United States
Validating machine learning hypothesis about soil thickness impacts on hydrologic fluxes using a process-based surface-subsurface model (393260)
Wen-Ping Tsai, Pennsylvania State University Main Campus, Civil and Environmental Engineering, University Park, PA, United States, Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States and Kuai Fang, Pennsylvania State University Main Campus, Civil and Environmental Engineering, University Park, United States
Drainage network generation using Deep Convolutional Generative Adversarial Networks (399819)
Yongwon Seo, Yeungnam University, Gyeongsan, South Korea, Sung Eun Kim, University of Hawaii at Manoa, Civil and Environmental Engineering, Honolulu, HI, United States, Hongkyu Yoon, Sandia National Laboratories, Geomechanics Department, Albuquerque, NM, United States and Jonghyun Harry Lee, University of Hawaii at Manoa, Civil and Environmental Engineering/Water Resources Research Center, Honolulu, HI, United States

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