H166
Machine Learning in Hydrologic Modeling III Posters

Tuesday, 15 December 2020: 04:00-20:59
Poster
Primary Convener:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States
Conveners:  Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, Hoshin Gupta, Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States and Frederik Kratzert, Johannes Kepler University, Institute for Machine Learning, Linz, Austria
Primary Liaison:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States
Chairs:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States, Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, Hoshin Gupta, Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States and Frederik Kratzert, Johannes Kepler University, Institute for Machine Learning, Linz, Austria
OSPA Liaison:  Grey Stephen Nearing, Google Research, Mountain View, CA, United States; University of California Davis, Land, Air, & Water Resources, Davis, CA, United States
 
Streamflow Predictions in Data-Scarce Basins using Bayesian and Physics-Informed Machine Learning Models (664667)
Dan Lu1, Goutam Konapala2, Scott L Painter1 and Shih-Chieh Kao1, (1)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States
 
A Comparison of In-Sample and Out-of-Sample Model Selection Approaches for ANN Streamflow Simulation (689633)
Xiaohan Mei, Texas A&M University College Station, Water Management and Hydrological Science, College Station, TX, United States and Patricia Smith, Texas A&M University College Station, Department of Biological and Agricultural Engineering, College Station, TX, United States
 
A Multivariate Bayesian Inference Model for Streamflow Prediction and Interpretation under Uncertainty (689474)
Kailong Li and Guohe Huang, University of Regina, Regina, SK, Canada
 
An optimized indirect method to estimate groundwater table depth anomalies over Europe based on Long Short-Term Memory networks (700610)
Yueling Ma1,2, Carsten Montzka1, Bagher Bayat1 and Stefan J Kollet1,2, (1)Forschungszentrum Jülich, Institute of Bio- and Geosciences, Agrosphere (IBG-3), Jülich, Germany, (2)Geoverbund ABC/J, Centre for High-Performance Scientific Computing in Terrestrial Systems, Jülich, Germany
 
Application of hydrometeorological indices for hydrologic forecasts within an artificial neural network framework (668368)
Renaud Jougla, University of Sherbrooke, Sherbrooke, QC, Canada and Robert Leconte, University of Sherbrooke, Civil and Building Engineering, Sherbrooke, QC, Canada
 
Application of physics informed neural networks to near-surface soil moisture dynamics (770828)
Toshiyuki Bandai, University of California Merced, Life & Environmental Sciences, Merced, CA, United States and Teamrat A Ghezzehei, University of California, Merced, Merced, CA, United States
 
Application of time-lapse imagery and machine learning to improve stream discharge monitoring (714982)
Kenneth Chapman1, Troy E Gilmore2, Christian D Chapman3, Mehrube Mehrubeoglu4 and Aaron R. Mittelstet2, (1)University of Nebraska Lincoln, School of Natural Resources, Lincoln, NE, United States, (2)University of Nebraska - Lincoln, Biological Systems Engineering Department, Lincoln, NE, United States, (3)MIT Lincoln Laboratory, Lexington, United States, (4)Texas A&M University Corpus Christi, Electrical Engineering, Corpus Christi, TX, United States
 
Assessing the Impacts of Extreme Precipitation Change on Flooding in the Northeastern United States (680408)
Charlotte Cockburn1, Jonathan Winter2, Erich C Osterberg1 and Frank J Magilligan2, (1)Dartmouth College, Department of Earth Sciences, Hanover, NH, United States, (2)Dartmouth College, Department of Geography, Hanover, NH, United States
 
Behavior of Internal Variables of Long and Short-Term Memory Neural Network for Rainfall-Runoff Modeling (669942)
Kazuki Yokoo1, Kei Ishida2, Takeyoshi Nagasato3, Masato Kiyama1 and Motoki Amagasaki1, (1)Kumamoto University, Kumamoto, Japan, (2)University of California Davis, Davis, United States, (3)Kumamoto University, Department of Civil and Environmental Engineering, Kumamoto, Japan
 
Development of the salt application rate tool for winter road maintenance (686873)
Sepideh Emami Tabrizi, University of Guelph, Guelph, ON, Canada, Bahram Gharabaghi, University of Guelph, School of Engineering, Guelph, ON, Canada and Hani Farghaly, Ontario Ministry of Transportation, St. Catharines, ON, Canada
 
Efficiency of super learner combining boosted regression tree, deep neural network, and frequency ratio models for mineral water potential mapping (679287)
Sanghoon Lee1, Dugin Kaown2, Eun-Hee Koh2, Hye-Lim Lee2, Kyung-Seok Ko3 and Kang Kun Lee1, (1)Seoul National University, Seoul, Korea, Republic of (South), (2)Seoul National University, Seoul, South Korea, (3)KIGAM Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea
 
Error-correction of streamflow predictions from a global hydrological model using random forests (680320)
Youchen Shen1, Jessica Ruijsch1, Meng Lu1, Edwin Sutanudjaja2 and Derek Karssenberg1, (1)Utrecht University, Utrecht, Netherlands, (2)Utrecht University, Physical Geography, Utrecht, Netherlands
 
Forecasting daily reference evapotranspiration using the wavelet-Gaussian Process Regression (GPR) approach (694216)
Sayed M. Bateni1, Helaleh Khoshkam2, Masoud Karbasi3, Mohammad Valipour1, Tongren Xu4 and Essam Heggy5, (1)University of Hawaii at Manoa, Honolulu, HI, United States, (2)Karaj College of Environment, Civil and Environmental Engineering, Karaj, Iran, (3)University of Zanjan, Water Engineering Department, Faculty of Agriculture, Zanjan, Iran, (4)Faculty of Geographical Science, Beijing Normal University, Beijing, China, (5)University of Southern California, Electrical Engineering - Electrophysics, Los Angeles, CA, United States
 
Forecasting Stormwater Pond Dry-Weather Water Temperature Profiles Using the Group Method of Data Handling (729710)
Stephen Stajkowski1, Bahram Gharabaghi1 and Hani Farghaly2, (1)University of Guelph, School of Engineering, Guelph, ON, Canada, (2)Ontario Ministry of Transportation, St. Catharines, ON, Canada
 
From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data (764172)
Wen-Ping Tsai1, Chaopeng Shen1, Ming Pan2, Kathryn Lawson1, Jiangtao Liu1 and Dapeng Feng1, (1)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States, (2)Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States
 
Generalized streamflow forecast model using deep learning (738154)
Zhongrun Xiang and Ibrahim Demir, University of Iowa, Iowa City, IA, United States
 
Graph Convolutions with Wavelets for Stream Temperature Forecasting (757011)
Andrew McDonald1, Scott Haag2, Mike Campagna2 and Ali Shokoufandeh3, (1)Drexel University, College of Computing and Informatics, Philadelphia, PA, United States, (2)Academy of Natural Sciences of Drexel University, Patrick Center for Environmental Research, Philadelphia, PA, United States, (3)Drexel University, Department of Computer Science, Philadelphia, PA, United States
 
Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX (690826)
Andreas Wunsch1, Tanja Liesch1 and Stefan Broda2, (1)Karlsruhe Institute of Technology, Department of Civil Engineering, Geo and Environmental Sciences, Karlsruhe, Germany, (2)BGR Federal Institute for Geosciences and Natural Resources, Hannover, Germany
 
Hybrid Machine Learning Framework for Analyzing and Optimizing Real-time Soil Moisture Sensor Arrays (749833)
Weiyu Li1, Ziqi Li2, Qina Yan3, Haruko M Wainwright4, Haiyan Zhou5, Yuxin Wu6, Baptiste Dafflon4, Roelof Versteeg5 and Daniel Tartakovsky1, (1)Stanford University, Stanford, United States, (2)INSA Institut National des Sciences Appliquées, Lyon, France, (3)University of Illinois at Urbana Champaign, Urbana, IL, United States, (4)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (5)Subsurface Insights, Hanover, NH, United States, (6)Lawrence Berkeley National Lab, Berkeley, CA, United States
 
Identifying relationships between urban stormwater signatures and watershed characteristics using interpretable machine learning (774499)
Celina Balderas Guzman, University of California Berkeley, Landscape Architecture and Environmental Planning, Berkeley, CA, United States, Runzi Wang, University of Michigan Ann Arbor, School for Environment and Sustainability, Ann Arbor, MI, United States, Oliver Muellerklein, University of California Berkeley, Berkeley, United States, Matthew Smith, Florida International University, Miami, FL, United States and Caitlin G Eger, Syracuse University, Syracuse, NY, United States
 
Integration of Data, Numerical Inversion, and Unsupervised Machine Learning to Identify Hidden Geothermal Resources in Southwest New Mexico (710632)
Bulbul Ahmmed1, Velimir monty V Vesselinov2 and Maruti Mudunuru2, (1)Baylor University, Waco, TX, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States
 
Leveraging Flow Duration Information to Improve Streamflow Prediction in Data-sparse Regions with Deep Learning Models (763797)
Dapeng Feng and Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States
 
Long-short term memory (LSTM) neural network integrated with satellite datasets to simulate streamflow in transboundary river basins (667444)
Manh-Hung Le and Venkataraman (Venkat) Lakshmi, University of Virginia, Engineering Systems and Environment, Charlottesville, VA, United States
 
Machine learning of a hysteretic hydrological signature: A case study with the catchment sensitivity function (737933)
Minseok Kim1, Hannes H Bauser1,2 and Peter A A Troch1,3, (1)University of Arizona, Biosphere 2, Tucson, AZ, United States, (2)Heidelberg University, Institute of Environmental Physics, Heidelberg, Germany, (3)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States
 
Machine Learning Techniques for Estimation of Suspended Sediment Loading in Urban Watersheds (665446)
Mohammadreza Moeini and Mengistu Geza, South Dakota School of Mines and Technology, Civil and Environmental Engineering, Rapid City, SD, United States
 
Modeling the regional water storage for geographically isolated wetlands (684850)
Quan Cui1, Majid Iravani2 and Monireh Faramarzi1, (1)University of Alberta, Earth and Atmospheric Sciences, Edmonton, AB, Canada, (2)Alberta Biodiversity Monitoring Institute, Edmonton, AB, Canada
 
New Machine Learning Method for Integrated Subsurface Modeling (768080)
Wen Pan, University of Texas at Austin, Petroleum and Geosystems Engineering, Austin, TX, United States, Carlos Torres-Verdin, University of Texas at Austin, Petroleum and Geosystems Eng., Austin, TX, United States and Michael Pyrcz, University of Texas at Austin, Austin, TX, United States
 
Predicting Water Temperature Dynamics of Unmonitored Lake Systems with Meta Transfer Learning (713768)
Jared Willard1,2, Jordan Stuart Read2, Alison Appling2, Samantha Oliver2, Xiaowei Jia3, Paul C Hanson4, Hilary A Dugan4, Robert Ladwig4 and Vipin Kumar1, (1)University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, MN, United States, (2)USGS, Middleton, WI, United States, (3)University of Pittsburgh, Pittsburgh, PA, United States, (4)University of Wisconsin Madison, Madison, WI, United States
 
Recurrent Neural Networks for Predicting the Dynamic Response of Geothermal Reservoirs from Monitoring Data (774987)
Anyue Jiang1, Qin Zhen2, Behnam Jafarpour1, Trenton T Cladouhos3 and Jalal Zia3, (1)University of Southern California, Los Angeles, CA, United States, (2)University of Southern California, Los Angeles, United States, (3)Cyrq Energy Inc., Salt Lake City, UT, United States
 
Sensitivity Analysis of Input Feature Selection in Multi-Layer Perceptron Neural Network to Predict Groundwater Levels (676047)
Reetik Sahu1, Juliane Müller2, Jangho Park2, Charuleka Varadharajan3, Bhavna Arora4, Boris Faybishenko3 and Deb Agarwal5, (1)Lawrence Berkeley National Laboratory, Computational Sciences Area, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Center for Computational Science and Engineering, Berkeley, CA, United States, (3)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (4)Lawrence Berkeley National Laboratory, Energy Geosciences Division, Berkeley, CA, United States, (5)LBNL, Berkeley, CA, United States
 
Simulation of Regulated Streamflow using Noah‐MP Land Surface Model and Machine Learning (718188)
Mahdi Erfani, University of South Carolina, Civil Engineering, Columbia, SC, United States, Qian Cao, University of California Los Angeles, Los Angeles, CA, United States, Dennis P Lettenmaier, UCLA, Department of Geography, Los Angeles, CA, United States and Erfan Goharian, University of South Carolina, Civil and Environmental Engineering, Columbia, SC, United States
 
Soil moisture Modeling and Forecasting using Spatiotemporal Machine Learning Based Models (751453)
Mohamed ElSaadani, University of Louisiana at Lafayette, Lafayette, LA, United States, Ahmed Abdelhameed, University of Louisiana at Lafayette, Lafayette, United States and Emad H Habib, University of Louisiana at Lafayette, Civil Engineering, Lafayette, LA, United States
 
Transferring Learning Between Machine Learning and Physics-Based Approaches to Improve Characterization of Watershed Behavior. (738948)
Luis De la Fuente1, Hoshin Gupta1 and Grey Stephen Nearing2, (1)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (2)Natel Energy Inc, Upstream Tech, Alameda, CA, United States
 
Using Image-Based Deep Learning to Identify Levees from Elevation Data for National-Scale Flood Modeling in the United States (728330)
Catharine Brown, Elizabeth F Weller, Helen L. Smith, David J Wood and Simon Waller, JBA Risk Management, Skipton, United Kingdom
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