H14C:
Big Data and Machine Learning in Hydrology and Subsurface Flow and Transport II


Submit an Abstract to this Session


Session ID#: 62373

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:  Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, 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:

Big Data and Machine Learning Approaches for Subsurface Resource Management: Premises, Promises and Perils (Invited) (365630)
Srikanta Mishra, Battelle Columbus, Columbus, OH, United States
Separating Aleatoric and Epistemic Uncertainties of Time Series Deep Learning Models for Soil Moisture Predictions (468915)
Kuai Fang, Pennsylvania State University Main Campus, Civil and Environmental Engineering, University Park, United States, Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States and Daniel Kifer, Pennsylvania State University, Department of Computer Science & Engineering, University Park, United States
Data-driven Hydrological Models Using Gaussian Processes (378951)
Jules Kouatchou, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Craig Pelissier, NASA Goddard Space Flight Center, Computational and Information Science & Technology Office, Greenbelt, MD, United States, Dr. Grey Stephen Nearing, PHD, Upstream Tech, Tuscaloosa, United States, Daniel Duffy, NASA Center for Climate Simulation, Greenbelt, United States, Christa D Peters-Lidard, NASA GSFC, Greenbelt, United States and James V Geiger, Goddard Space Flight Center, Greenbelt, MD, United States
Hydrological Modeling in the Era of Big Data: A Computer Vision-Based Approach to Fusing Spatiotemporal Data (361755)
Shijie Jiang1,2 and Yi Zheng1, (1)Southern University of Science and Technology, School of Environmental Science and Engineering, Shenzhen, China, (2)National University of Singapore, Department of Civil and Environmental Engineering, Singapore, Singapore
Looking Beyond Data: Information-theory based criteria to evaluate different machine-learning approaches for hydrological problems (347331)
Benjamin Mewes1, Henning Oppel1, Andreas J Hartmann2 and Andreas H Schumann3, (1)Ruhr University Bochum, Chair of Hydrology, Water Resource Management & Environmental Engineering, Bochum, Germany, (2)University of Bristol, Bristol, United Kingdom, (3)Ruhr University Bochum, Bochum, Germany
Modelling depth of the redox interface in high resolution at national scale using machine learning and geostatistics (353717)
Julian Koch1, Simon Stisen2, Jens Christian Refsgaard1, Ernstsen Vibeke3, Peter Roll Jakobsen3 and Anker L Højberg1, (1)Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark, (2)Geological Survey of Denmark and Greenland, Department of Hydrology, København K, Denmark, (3)Geological Survey of Denmark and Greenland, Department of Geochemistry, Copenhagen, Denmark
Mining information from collections of papers:Illustrative analysis of groundwater and disease (359633)
Yiding Zhang, The Ohio State University, Environmental Sciences Graduate Program, Columbus, OH, United States, Xiaonan Ji, The Ohio State University, Columbus, OH, United States, Motomu Ibaraki and Frank W Schwartz, The Ohio State University, School of Earth Sciences, Columbus, OH, United States

See more of: Hydrology