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GSA 2020 Connects Online

Session No. 212
Thursday, 29 October 2020: 1:30 PM-5:30 PM

T185. Machine Learning in Hydrogeology

GSA Hydrogeology Division; Consortium of Universities for the Advancement of Hydrologic Science, Inc.; GSA Karst Division

 

Paul E. Stackelberg, Kenneth Belitz, Mason O. Stahl and James J. Butler Jr., Advocates
Paper #
Start Time
 
1:30 PM
Introductory Remarks
212-1
1:35 PM
NOVEL HYDROGEOLOGICAL PREDICTIONS AND INFERENCES USING MACHINE LEARNING ALGORITHMS: THREE ILLUSTRATIVE EXAMPLES
JAMEEL, Mohd Yusuf1, STAHL, Mason O.2, GEHRING, Jaclyn2 and VALLE, Denis3, (1)Civil and Environmental Engineering, Massachusetts Institute of Technology, 280 Vassar St, Apartment J2, Cambridge, MA 02139, (2)Department of Geology, Union College, Schenectady, NY 12308, (3)School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603
212-2
1:50 PM
HIGH-RESOLUTION NATIONAL-SCALE HYDROGEOLOGIC DATASETS IN SUPPORT OF MACHINE LEARNING MODELS
BELITZ, Kenneth, Water Mission Area - Earth System Processes Division - Water Assessments Branch, U.S. Geological Survey, Carlisle, MA 01741
212-3
2:05 PM
USING MACHINE-LEARNING PREDICTIONS OF GROUNDWATER SALINITY TO ASSESS WATER AVAILABILITY IN THE MISSISSIPPI RIVER VALLEY ALLUVIAL AQUIFER
KILLIAN, Courtney, U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, 308 Airport Road S., Pearl, MS 39208 and KNIERIM, Katherine J., U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, 401 Hardin Road, Little Rock, AR 72211
212-4
2:20 PM
DOWNSCALING OF GRACE TWSA IN HIGHLY STRESSED AQUIFER SYSTEMS, THE CENTRAL VALLEY AQUIFER, USING MACHINE LEARNING
ASFAW, Dawit Wolday, Geography-Geology-Environment, Illinois State University, 101 S School Street, Normal, IL 61761; Dept. of Geography, Geology, and the Environment, Illinois State University, Normal, IL 61790 and SEYOUM, Wondwosen Mekonnen, Dept. of Geography, Geology, and the Environment, Illinois State University, Normal, IL 61761
 
2:35 PM
Discussion
 
2:50 PM
Introductory Remarks
212-5
2:55 PM
GHOST IN THE MACHINE: USING ARTIFICIAL INTELLIGENCE, TRAINED ON GROUNDWATER MODELS, FOR RAPID DECISION SUPPORT (Invited Presentation)
FIENEN, Michael N., Upper Midwest Water Science Center, U.S. Geological Survey, 8505 Research Way, Middleton, WI 53562
212-6
3:10 PM
COUPLED PHYSICAL SIMULATION (PS) AND MACHINE LEARNING (ML) FOR MAPPING GROUNDWATER AGE
STARN, J. Jeffrey, US Geological Survey, 101 Pitkin Street, East Hartford, CT 06108
212-7
3:25 PM
USING MACHINE LEARNING TO PREDICT HIGH ARSENIC OR HIGH MANGANESE AT DRINKING WATER DEPTHS OF THE GLACIAL AQUIFER SYSTEM, NORTHERN CONTINENTAL US
ERICKSON, Melinda L.1, ELLIOTT, Sarah M.1, STACKELBERG, Paul E.2, BROWN, Craig J.3, RANSOM, Katherine M4 and REDDY, James E.5, (1)U.S. Geological Survey, Upper Midwest Water Science Center, 2280 Woodale Drive, Mounds View, MN 55112, (2)U.S. Geological Survey, 425 Jordan Road, Troy, NY 12180, (3)New England Water Science Center, U.S. Geological Survey, 101 Pitkin Street, East Hartford, CT 06108, (4)California Water Science Center, U.S. Geological Survey, 6000 J St., Sacramento, CA 95819, (5)U.S. Geological Survey, 30 Brown Road, Ithaca, NY 14850-1573
212-8
3:40 PM
FUZZY RULE-BASED SYSTEMS FOR MULTIVARIATE AND UNIVARIATE HYDROLOGICAL FORECASTING
FAYBISHENKO, Boris1, MUELLER, Juliane2, SAHU, Reetik2, PARK, Jangho2, ARORA, Bhavna3, VARADHARAJAN, Charuleka4 and AGARWAL, Deborah2, (1)Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720; Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74-316C, Berkeley, CA 94720, (2)Computer Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, (3)Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74-316C, Berkeley, CA 94720, (4)Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74-316C, Berkeley, CA 94720
 
3:55 PM
Discussion
 
4:10 PM
Introductory Remarks
212-9
4:15 PM
PROCESS-GUIDED DEEP LEARNING FOR WATER TEMPERATURE PREDICTION (Invited Presentation)
APPLING, Alison P.1, JIA, Xiaowei2, WILLARD, Jared3, OLIVER, Samantha K.4, SADLER, Jeffrey M.1, ZWART, Jacob A.1, READ, Jordan S.1 and KUMAR, Vipin3, (1)US Geological Survey, Data Science Branch, Integrated Information Dissemination Division, State College, PA 16803, (2)Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, (3)Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, (4)Upper Midwest Water Science Center, US Geological Survey, Middleton, WI 53562
212-10
4:30 PM
SUBSURFACE PARAMETER UNCERTAINTY QUANTIFICATION USING ENVIRONMENTAL TRACERS AND SURROGATE MODELING METHODS
THIROS, Nicholas E. and GARDNER, W. Payton, Geosciences, University of Montana, 32 Campus Drive #1296, Missoula, MT 59812-1296
212-11
4:45 PM
FLOODING DRIVES GROUNDWATER ARSENIC CONTAMINATION IN SOUTHEAST ASIA
CONNOLLY, Craig T.1, STAHL, Mason O.2, DEYOUNG, Beck2 and BOSTICK, Benjamin C.3, (1)Columbia University, Lamont-Doherty Earth Observatory, 61 Rte 9W, Palisades, NY 10964, (2)Department of Geology, Union College, Schenectady, NY 12308, (3)Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9w, Palisades, NY 10964
212-12
5:00 PM
MACHINE LEARNING TO CHARACTERIZE REGIONAL GEOTHERMAL RESERVOIRS IN THE WESTERN USA
AHMMED, Bulbul1, VESSELINOV, Velimir2 and MUDUNURU, Maruti K.2, (1)Computational Earth Science, Los Alamos National Laboratory, Los Alamos, NM 87545; Department of Geosciences, Baylor University, D 409, BSB, Waco, TX 76706, (2)Computational Earth Science, Los Alamos National Laboratory, Los Alamos, NM 87545
 
5:15 PM
Discussion
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