H008
Advances in Machine Learning for Earth Science: Observation, Modeling, and Applications

Session ID#: 159915

Session Description:
Machine learning (ML) methods have shown tremendous potential and advances in the understanding of Earth science. New datasets and ML models with high spatial and temporal resolutions are emerging at an unprecedented rate, which has opened up various new avenues of research in the field. This session aims to engage diverse earth scientific communities to share their novel ML methods and applications on radar & satellite observations, data fusion, Earth system modeling & forecast, natural hazard and extreme events, climate projection, environmental sustainability, explainable AI, etc. The studies with generalizable results are strongly encouraged.
Index Terms:

1855 Remote sensing [HYDROLOGY]
1942 Machine learning [INFORMATICS]
3354 Precipitation [ATMOSPHERIC PROCESSES]
4313 Extreme events [NATURAL HAZARDS]

Primary Convener:  Yixin Wen, University of Florida, Department of Geography, Ft Walton Beach, FL, United States
Conveners:  Guoqiang Tang, University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada, Mengye Chen, The University of Oklahoma, Norman, OK, United States and Phu Nguyen, University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States
 
Applying Machine Learning Methods in Quality Assurance Quality Control (QAQC) Protocols for The Internet of Things (IoT) Based Environment Monitoring Platforms (1045906)
Ziwen Yu1, Chi Zhang2, Songzi Wu2, Morgan Gunter2, Beck Peyton2, Kevin Denis2, Franco A Montalto3, Alex Bedig4, Marcus Quigley5 and William Lusher2, (1)University of Florida, Ft Walton Beach, FL, United States, (2)University of Florida, Gainesville, United States, (3)Drexel University, Civil, Architectural and Environmental Engineering, Philadelphia, United States, (4)OptiRTC, Boston, United States, (5)OptiRTC, Boston, MA, United States
 
A Framework for Streamflow Short-term Forecasting and Long-term Projection based on Long Short-Term Memory and Bayesian Optimization (1052867)
Jongho Kim, University of Ulsan, School of Civil and Environmental Engineering, Ulsan, South Korea and Trung Duc Tran, University of Ulsan, Ulsan, South Korea
 
3D Reconstruction of Multi-layer cloud masks using Advanced Baseline Imager (ABI) Observations trained on CloudSat/CALIPSO data using a deep neural network approach (Invited) (1056453)
Jie Gong1,2, Yiding Wang3, Leah Ding3 and Dong Liang Wu4, (1)NASA Goddard Space Flight Center, Greenbelt, United States, (2)University of Maryland Baltimore County, Baltimore, MD, United States, (3)American University, Department of Computer Science, Washington, D. C., United States, (4)Climate and Radiation Laboratory, NASA/GSFC, Greenbelt, MD, United States
 
Deep-learning emulation and upscaling of a process-based hydrological and water quality model in the Upper Mississippi River Basin (1057257)
Hadi Mohammed, University of Kansas, Civil, Environmental, and Architectural Engineering, Lawrence, KS, United States and Amy Hansen, University of Kansas, Civil, Environmental, and Architectural Engineering, Lawrence, United States
 
Machine Learning Enhanced Snowfall Rate Retrievals from Passive Microwave Sensors (Invited) (1061987)
Yongzhen Fan, University of Maryland College Park, Earth System Science Interdisciplinary Center, College Park, MD, United States, Huan Meng, Natl Oceanic & Atmospheric Adm, College Park, MD, United States, Jun Dong, University of Maryland College Park, Earth System Science Interdisciplinary Center, College Park, United States, Cezar Kongoli, University of Maryland, College Park, Earth System Science Interdisciplinary Center, College Park, MD, United States, Yalei You, University of Maryland College Park, Earth System Science Interdisciplinary Center, College PARK, MD, United States and Ralph R Ferraro, NOAA/NESDIS, College Park, United States
 
Super-resolution of Land Surface Temperature Using Deep Learning (1062679)
Anna Boser, University of California Santa Barbara, Berkeley, CA, United States, Ryan Stofer, University of California Santa Barbara, Santa Barbara, United States and Kelly K Caylor, University of California Santa Barbara, Department of Geography, Bren School of Environmental Science and Management, Earth Research Institute, Santa Barbara, CA, United States
 
Uncertainty Quantification of Machine Learning Models to Improve Streamflow Prediction in Changing Climate and Environmental Conditions (1062703)
Dan Lu, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Siyan Liu, Oak Ridge National Laboratory, Oak Ridge, United States, Scott L Painter, Los Alamos National Laboratory, Los Alamos, United States; Oak Ridge National Laboratory, Watershed Systems Modeling Group, Environmental Sciences Division, Oak Ridge, United States, Natalie Griffiths, Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States and Eric M Pierce, Oak Ridge National Laboratory, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge, United States
 
Using Neural-Network Based Emulators to find Optimal Ocean Carbon Cycle Parameters (1062981)
Lucas Gloege1, Anastasia Romanou2 and Paul Lerner1, (1)NASA Goddard Institute for Space Studies, New York, NY, United States, (2)NASA Goddard Institute for Space Studies, and Dept of Applied Phys. and Applied Math., Columbia University, New York, NY, United States
 
Using Remote Earth Observations and In Situ Observations to Impute Sparse Groundwater Data at Individual Wells with Machine Learning (1064298)
Saul Ramirez1, Gustavious P Williams2 and Norm Jones1, (1)Brigham Young University, Civil and Construction Engineering, Provo, UT, United States, (2)Brigham Young University, Civil and Construction Engineering, Provo, United States
 
LSTM-based meta-learning framework to simulate water temperature in under-observed paddy fields with few-shot monitoring data (1065798)
Wenpeng Xie1, Masaomi Kimura2 and Kyoji Takaki1, (1)The University of Tokyo, Graduate School of Agricultural and Life Sciences, Tokyo, Japan, (2)Kindai University, Department of Environmental Management, Faculty of Agriculture, Nara, Japan
 
Detecting Flooded Vegetation in Wetlands with Convolutional Neural Networks Integrating Multispectral and SAR Images (1071828)
Francisco J. Peña, Stockholm University, Stockholm, Sweden and Fernando Jaramillo, Stockholm University, Department of Physical Geography and Baltic Sea Centre, Stockholm, Sweden
 
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning (1076675)
Griffin Mooers1, Mike S Pritchard2, Tom Beucler3, Prakhar Srivastava4, Harshini Mangipudi4, Liran Peng1 and Stephan Mandt4, (1)University of California Irvine, Earth System Science, Irvine, CA, United States, (2)University California Irvine, Earth System Science, Irvine, United States, (3)University of Lausanne, Institute of Earth Surface Dynamics, Lausanne, Switzerland, (4)University of California Irvine, Computer Science, Irvine, United States
 
Multi-step Ahead Daily Reference Evapotranspiration Forecasting by Hybrid Machine Learning and Deep Learning Models in Different Climate Regions Across the Contiguous United States (1077904)
Helaleh Khoshkam, University of Hawaii at Manoa, Department of Civil and Environmental Engineering, Honolulu, United States, Mohammad Valipour, Metropolitan State University of Denver, Department of Engineering and Engineering Technology, Denver, United States, Sayed M. Bateni, University of Hawaii at Manoa, Civil and Environmental Engineering and Water Resources Research Center, Honolulu, HI, United States and Changhyun Jun, Chung-Ang University, Department of Civil and Environmental Engineering, Seoul, South Korea
 
K-means clustering of the spatial distribution of rainfall over the Korean peninsula (1080259)
Song-Lak Kang, Gangneung-Wonju National University, Atmospheric & Environmental Sciences, Gangneung, Korea, Republic of (South) and Jae-Heon Lee, Gangneung-Wonju National University, Department of Atmospheric & Environmental Sciences, Gangneung-si, South Korea
 
Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders (1083420)
Yueya Wang, Hong Kong University of Science and Technology, ENVR, Hong Kong, Hong Kong, Xiaoming Shi, Hong Kong University of Science and Technology, Hong Kong, China, Lili Lei, Nanjing University, School of Atmospheric Sciences, Nanjing, China and Jimmy Chi-Hung Fung, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
 
Spatiotemporal imputation of MODIS Normalized Difference Vegetation Index (NDVI) data using machine learning techniques (1084873)
Sayed M. Bateni, University of Hawaii at Manoa, Department of Civil and Environmental Engineering and Water Resources Research Center, Honolulu, United States, Trang Thi Kieu Tran, University of Hawaii at Manoa, Civil and Environmental Engineering, Honolulu, United States, Hamid Mohebzadeh, School of Engineering, Guelph, ON, Canada, Changhyun Jun, Chung-Ang University, Department of Civil and Environmental Engineering, Seoul, South Korea and Thomas W Giambelluca, University of Hawaii at Manoa, Water Resources Research Center, Honolulu, HI, United States
 
Prediction and Spatial Interpolation of the groundwater Level on Jeju Island Using Machine Learning Model (1085256)
Sim DaeCheon, Kangwon National University, Chuncheon, Korea, Republic of (South)
 
Prediction of Rainfall Response to the 21st-century Climate Change in Ghana using Machine Learning Empirical Statistical Downscaling (1090867)
Frank Arthur, University of South-Eastern Norway, Department of Natural Sciences and Environmental Health, Bø, Norway, Daniel Boateng, University of Tübingen, Department of Geosciences, Tübingen, Germany and Michael Baidu, University of Leeds, Institute of Climate and Atmospheric Science, School of Earth and Environment, Leeds, United Kingdom
 
Generalized model for wet/dry shoreline detection and total water level elevation using deep learning (1092757)
Marina Vicens Miquel1, F. Antonio Medrano2, Hamid Kamangir2 and Michael J Starek3, (1)Texas A&M University Corpus Christi, Corpus Christi, TX, United States, (2)Texas A&M University - Corpus Christi, Corpus Christi, United States, (3)Texas A&M University at Corpus Christi, Corpus Christi, United States
 
Climate Change, Machine Learning, and Space-Time Symmetry (1093315)
Abhinav Gupta, Desert Research Institute, Las Vegas, Division of Hydrologic Sciences, Las Vegas, United States
 
Using Machine Learning to Automate Trend Model Evaluations for Large-scale, Multi-site Water-quality Trend Assessments (1093876)
Jennifer Murphy, USGS Central Midwest Water Science Center, DeKalb, IL, United States and Jeffrey G Chanat, U.S. Geological Survey, Virginia Water Science Center, Richmond, United States
 
Predicting regional heatwaves using Interpretable Graph Neural Networks (1094634)
Yin Yu, Organization Not Listed, Washington, DC, United States; Pennsylvania State University Main Campus, University Park, PA, United States, Peiyuan Li, University of Illinois at Urbana Champaign, Illinois State Water Survey, Prairie Research Institute, Champaign, United States, Daning Huang, Pennsylvania State University Main Campus, University Park, United States, Ashish Sharma, University of Illinois at Urbana Champaign, Illinois State Water Survey and Department of Atmospheric Sciences, Champaign, IL, United States and Zhihua Wang, Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, United States
 
H2OvalNet: Detecting Fairy Circles from Sentinel-2 Imagery in Australia (1094759)
Wei Ji Leong1, Bidhyananda Yadav1, Adelaide Negrete1, Ian M Howat1 and Joachim Moortgat2, (1)Ohio State University, Byrd Polar and Climate Research Center, Columbus, United States, (2)Ohio State University Main Campus, School of Earth Sciences, Columbus, OH, United States
 
A Simple Characterization of Sea Surface Temperature Patterns that Represent the Seasonal Evolution of El Niño Southern Oscillation Flavors (1096065)
Julia Mindlin, Universidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, Buenos Aires, Argentina, Rishav Goyal, University of New South Wales, Sydney, NSW, Australia, Santiago Ignacio Hurtado, Instituto de Investigaciones fFrestales y Agropecuarias Bariloche )IFAB=, INTA-CONICET, San Carlos de Bariloche, Argentina; School of Astronomy and Geophysics, National University of La Plata, La Plata, Argentina, Renata G Tedeschi, INPE, Cachoeira Paulista, SP, Brazil and Marcia Zilli, University of Oxford, School of Geography and the Environment, Oxford, United Kingdom
 
Using Machine Learning with Partial Dependence Analysis to Investigate Land-Atmosphere Coupling (1100711)
Jared Trok1, Frances V Davenport2, Elizabeth A Barnes2 and Noah S Diffenbaugh3, (1)Stanford University, Earth System Science, Stanford, United States, (2)Colorado State University, Atmospheric Science, Fort Collins, United States, (3)Stanford University, Stanford, CA, United States
 
The Usefulness of Normalized Satellite Indices for Machine Learning Classification of Riparian Land Covers (1102555)
Hitoshi Miyamoto1, Takuya Sato1,2 and Shuji Iwami2, (1)Shibaura Institute of Technology, Civil Engineering, Tokyo, Japan, (2)CTI Engineering Co., Ltd., Tokyo, Japan
 
Utilize Convolutional Neural Networks to Forecast Regional Groundwater Levels – A Case Study in the Jhuoshuei River Basin of Taiwan (1105336)
Yu-Wen Chang, Chu-Han Chen and Fi-John Chang, National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan
 
Application of Deep Learning Based Long Short Term Memory (LSTM) Model on the Prediction of Climate Variables Under Changing Climate Conditions (1107997)
MD Abul Ehsan Bhuiyan and Wassila Mamadu Thiaw, NOAA/NWS/NCEP, Climate Prediction Center, College Park, MD, United States
 
Assessing the Predictability of Short-term Terrestrial Water Storage Variations Using Deep Learning Methods (1108280)
Alexander Y Sun1, Ashraf Rateb2, Himanshu Save3, Bridget R Scanlon2 and Emad Hasan4, (1)University of Texas at Austin, Austin, TX, United States, (2)University of Texas at Austin, Bureau of Economic Geology, Jackson School of Geosciences, Austin, TX, United States, (3)Center for Space Research, University of Texas at Austin, Austin, United States, (4)SUNY at Binghamton, HyDROS (University of Oklahoma), Binghamton, United States
 
Generative Adversarial Network for Real-time Drought Monitoring (1111198)
Ehsan Foroumandi1, Keyhan Gavahi1 and Hamid Moradkhani2, (1)University of Alabama, Civil, Construction, and Environmental Engineering, Tuscaloosa, United States, (2)The University of Alabama, Civil, Construction, and Environmental Engineering, Tuscaloosa, United States
 
A water isotopic modelling library for environmental studies (1117276)
Ashkan Hassanzadeh, IDAEA-CSIC, Barcelona, Spain, Sonia Valdivielso, Spanish National Research Council (CSIC), Barcelona, Spain, Enric Vazquez-Sune, IDAEA CSIC, Geosciences, Barcelona, Spain and Rotman Criollo, Mediterranean Institute for Advanced Studies (IMEDEA, CSIC-UIB), Esporles - Illes Balears, Spain
 
Harmful algal blooms prediction in coastal areas with environmental stressors using machine learning Methods: Case study of Biscayne Bay, South Florida (1119584)
Zhengxiao Yan and Nasrin Alamdari, Florida State University, Tallahassee, United States
 
Subseasonal Prediction of Energy Consumption using Long Short-Term Memory Models (1119723)
Samuel Li, Champaign, IL, UNITED STATES and Ryan L Sriver, University of Illinois, Urbana, IL, United States
 
The Estimation of Water Table Depth over the Contiguous US Using Machine Learning Methods (1128391)
Yueling Ma1, Elena Leonarduzzi2, Amy Defnet1, Peter Melchior3, Laura E Condon4 and Reed M Maxwell5, (1)Princeton University, High Meadows Environmental Institute, Princeton, United States, (2)Princeton University, High Meadows Environmental Institute, Princeton, NJ, United States, (3)Princeton University, Astrophysical Sciences and the Center for Statistics and Machine Learning, Princeton, NJ, United States, (4)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (5)Princeton University, Civil & Environmental Engineering, High Meadows Environmental Institute, Princeton, United States
 
A Deep Learning Approach for Detection, Semantic Segmentation and Density Classification of Smoke in Satellite Imagery (1131851)
Rey Koki1, Jebb Stewart2, Christina Kumler3 and Michael McCabe1, (1)University of Colorado at Boulder, Computer Science, Boulder, United States, (2)NOAA Global Systems Laboratory, Boulder, United States, (3)NOAA Global Systems Laboratory and Cooperative Institute for Research in Environmental Sciences, Boulder, United States
 
Super-Resolution Deep Neural Networks for Water Classification at 2-m Resolution from Sentinel-2 Satellite Imagery (1133083)
Ziwei Li1, Joachim Moortgat2, Wei Ji Leong3, Chunli Dai4, Ian Howat5, Bidhyananda Yadav3 and Michael T Durand6, (1)Ohio State University Main Campus, Columbus, United States, (2)Ohio State University Main Campus, School of Earth Sciences, Columbus, OH, United States, (3)Ohio State University, Byrd Polar and Climate Research Center, Columbus, United States, (4)Ohio State University Main Campus, Division of Geodetic Science, School of Earth Sciences, Columbus, OH, United States, (5)Ohio State University, Byrd Polar & Climate Research Center, Columbus, United States, (6)Byrd Polar and Climate Research Center, Columbus, United States
 
Transfer Learning and Predictor Selector Algorithm for Developing a Generalizable Pedo-Transfer Function for Saturated Hydraulic Conductivity (1135104)
Suraj Jena, Ph.D. Scholar, School of Infrastructure, Bhubaneswar, India, Binayak Mohanty, Texas A&M University, Department of Biological and Agricultural Engineering, College Station, TX, United States, Rabindra K. Panda, Professor, School of Infrastructure, Department of Civil Engineering, Bhubaneswar, India and Meenu Ramadas, Indian Institute of Technology Bhubaneswar, Department of Civil Engineering, School of Infrastructure, Bhubaneswar, India
 
Assessment of a deep learning model in rainfall nowcasting using synthetic rainfall scenarios (1145807)
Ahmed Abdelhalim, University of Bristol, Bristol, BS8, United Kingdom, Miguel Angel Rico-Ramirez, University of Bristol, Department of Civil Engineering, Bristol, United Kingdom and Dawei Han, University of Bristol, CIVIL ENGINEERING, Bristol, United Kingdom
 
Open Classification of Regimes in the Southeast USA: An open-source machine learning classifier for aerosol and meteorological regimes for ARM Mobile Facility deployments. (1147913)
Robert Clyde Jackson1, Die Wang2, Scott M Collis1 and Michael P Jensen3, (1)Argonne National Laboratory, Argonne, IL, United States, (2)Brookhaven National Laboratory, Upton, NY, United States, (3)Brookhaven National Laboratory, Upton, United States
 
Mapping the tectonic boundaries using statistical data analysis and its preliminary application to the US and Antarctica (1148623)
Mengjie Zheng1,2, Weisen Shen2, Siyuan Sui2 and Tao Xu1, (1)Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China, (2)Stony Brook University, Department of Geosciences, Stony Brook, New York, United States
 
Machine Learning Forecasting for Streamflow in West-Central Florida using Climate Drivers (1150990)
Jia-Yi Ling1, Christopher J Martinez1 and Nikolay Bliznyuk2, (1)University of Florida, Gainesville, United States, (2)University of Florida, Gainesville, FL, United States
 
AI-based Reconstruction of the Global Industrial Water Demand Map (1151690)
Manas Ranjan Panda, Yonsei University, Seoul, Korea, Republic of (South) and Yeonjoo Kim, Yonsei University, Department of Civil and Environmental Engineering, Seoul, Korea, Republic of (South)
 
A Rainfall-Runoff Model with Multi-State-Vector Compound-Cell RNN Based on LSTM and Sequence-to-Sequence Learning (1153116)
Quanyu Wang, Xi'an, Shaanxi, CHINA and Tian Lan, Chang'an University, Xi'an, China
 
Training and validating deep learning models for urban flood mapping using public and commercial satellite sensors (1154043)
Rohit Mukherjee1, Zhijie Zhang2, Matthew Purri3, Hannah Friedrich4, Jonathan Giezendanner2, Upmanu Lall5 and Beth Tellman1, (1)University of Arizona, School of Geography, Development and Environment, Tucson, AZ, United States, (2)University of Arizona, School of Geography, Development and Environment, Tucson, United States, (3)University of Arizona, School of Geography, Development, and Environment, Tucson, United States, (4)University of Arizona, Tucson, AZ, United States, (5)Columbia University, Earth and Environmental Engineering, New York, United States
 
Investigating the effect of typhoon track on rainfall spatial distribution over a watershed using artificial intelligence technique (1155424)
Gary Wee and Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan
 
Real-time prediction and correction of regional flooding by combining artificial intelligence and IoT sensors. (1155436)
Shun-Nien Yang1, Li-Chiu Chang1 and Fi-John Chang2, (1)Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan, (2)National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan
 
Potential of Transfer Learning for Mapping Supraglacial Stream Drainage Patterns in Northwest Greenland (1156792)
Sarah Esenther, Brown University, Providence, RI, United States, Mason N Lee, Brown University, Providence, United States, Kaiyuan Wang, McGill University, Earth and Planetary Sciences, Montreal, QC, Canada, Chanatip Vongkitbuncha, The Peddie School, Hightstown, NJ, United States, Alexandra Boghosian, LDEO of Columbia University, Palisades, United States and Laurence C Smith, Brown University, Department of Earth, Environmental and Planetary Sciences, Providence, RI, United States
 
Impacts of Hydrometeorological Factors on Forecasting Soil Moisture Via Remote Sensing Observations and Deep Learning – A Quinquennial Study (1158939)
Archana Kannan1, Grigorios Tsagkatakis2 and Mahta Moghaddam1, (1)University of Southern California, Ming Hsieh Department of Electrical and Computer Engineering, Los Angeles, CA, United States, (2)Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Heraklion, Greece
 
Applying Machine Learning Techniques to Forecasting Fire Behavior – AIFire (1158962)
Janaina Mayara Pinto Nascimento1, Alessandro Lindahl Banducci2, Jonana Romero-Alvarez3, Ravan Ahmadov3, Isidora Jankov4 and Georg Grell5, (1)NOAA/GSL and National Academies of Sciences, Engineering and Medicine, Boulder, United States, (2)Colorado State University, Physics, Fort Collins, United States, (3)CU/CIRES and NOAA/GSL, Boulder, CO, United States, (4)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (5)NOAA ESRL/GSL, Boulder, United States
 
Feature-Attention Transformer Neural Networks for Multi-Step Environmental Time Series Forecasting (1159144)
Enrique Orozco Lopez, University of Florida, Ft Walton Beach, FL, United States and David A Kaplan, University of Florida, Environmental Engineering Sciences, Gainesville, United States
 
Feature Pathway Detection using Random Forest Regressor Feature Importances (1159285)
Matthew G Peterson1, Jeffrey Jake Nichol1, Irina Tezaur2, Warren Leon Davis IV3 and Max Carlson4, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)Sandia National Laboratories, Livermore, United States, (3)NASA Langley Research Center, Hampton, VA, United States, (4)Sandia National Labratories, Albuquerque, United States
 
Using different XAI baselines to answer different science questions (1167929)
Antonios Mamalakis1, Elizabeth A Barnes1 and Imme Ebert-Uphoff2, (1)Colorado State University, Atmospheric Science, Fort Collins, United States, (2)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
 
A Machine-Learning Model of Terrestrial Water Storage Changes at the Global Scale (1171919)
Irene Palazzoli1,2, Wenli Zhao3, Serena Ceola1 and Pierre Gentine3, (1)University of Bologna, Department of Civil, Chemical, Environmental and Materials Engineering, Bologna, Italy, (2)Columbia University, Department of Earth and Environmental Engineering, New York, United States, (3)Columbia University, Department of Earth and Environmental Engineering, New York, NY, United States
 
Identifying the Hydrometeorological Decision Factors Influencing Reservoir Releases over the Upper Colorado Region using a Long Short-Term Memory Network (1172422)
Ming Fan1, Lujun Zhang2, Siyan Liu3, Tiantian Yang2 and Dan Lu1, (1)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (2)The University of Oklahoma, Norman, United States, (3)Oak Ridge National Laboratory, Oak Ridge, United States
 
Identification of Oceanic Mesoscale Cold Pools Using a Convolutional Neural Network Trained on Ocean Vector Winds (1173034)
Stephen McNeal1, Iksha Gurung1, Muthukumaran Ramasubramanian1, Aaron S Kaulfus2, Timothy J Lang2, Stephen W Nesbitt3 and Rahul Ramachandran2, (1)University of Alabama in Huntsville, Huntsville, AL, United States, (2)NASA Marshall Space Flight Center, Huntsville, United States, (3)University of Illinois at Urbana-Champaign, Department of Atmospheric Sciences, Urbana, United States
 
Deep learning sub-seasonal predictions of flooded area in South Sudan (1177541)
Jonathan M Frame1, Max Goodman1, Subit Chakrabarti1, Emmalina Glinskis1, Annalee Tai1, Rogerio Bonifacio2, Sebastian Boeck2 and Beth Tellman3, (1)Cloud to Street, New York, United States, (2)World Food Program, Rome, Italy, (3)Cloud to Street, New York, NY, United States
 
Automated Machine Learning Approach to Supervised Anomaly Detection from Critical Zone Watershed Sensor-Generated Time Series Data (1178125)
Ijaz UL HAQ, University of Vermont, Burlington, VT, United States and Byung Suk Lee, University of Vermont, Computer Science, Burlington, VT, United States
 
Precipitation Estimation over the Western US Using a U-Net Convolutional Neural Network Model (1180677)
Claudia Jimenez Arellano, Vesta Afzali Gorooh, Phu Nguyen and Soroosh Sorooshian, University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States
 
Big data analytics and its role to support groundwater management in Southern Africa (1181845)
Zaheed Gaffoor1, Kevin Pietersen2, Antoine Bagula3, Nebo Jovanovic4 and Thokozani Kanyerere4, (1)IBM Research Africa, Johannesburg, South Africa, (2)Institute for Water Studies, University of the Western Cape, Cape Town, South Africa, (3)Department of Computer Science, University of the Western Cape, Cape Town, South Africa, (4)Department of Earth Sciences, University of the Western Cape, Cape Town, South Africa
 
Opportunities for using Artificial Intelligence and Machine Learning to Address Hydrological Grand Challenges (1183660)
Charuleka Varadharajan1, Scott L Painter2, Jitendra Kumar3, Dan Lu4, Chaopeng Shen5, Xingyuan Chen6, John D Moulton7, Soumendra Nath Bhanja8, Wen-Ping Tsai5, Dapeng Feng5, Mohammed Ombadi1, Helen Weierbach9, Jared Willard10, Wei Zhi5 and Alexander Y Sun11, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)Los Alamos National Laboratory, Los Alamos, United States, (3)Oak Ridge National Laboratory, Oak Ridge, United States, (4)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (5)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, (6)Pacific Northwest National Laboratory, Richland, WA, United States, (7)Los Alamos National Laboratory, Los Alamos, NM, United States, (8)Oak Ridge National Laboratory, Watershed Systems Modeling Group, Environmental Sciences Division, Oak Ridge, TN, United States, (9)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, United States, (10)University of Minnesota, Minneapolis, MN, United States, (11)University of Texas at Austin, Austin, TX, United States
 
Interactive Deep-Learning Modeling of Snow Water Equivalent Through Application of The National Snow Model With Geoweaver. (1185490)
Dane Liljestrand1, Ryan Johnson2, Karnesh Jain3, James Halgren2, Carlos Oroza4 and Steven J Burian5, (1)University of Utah, Civil and Environmental Engineering, Salt Lake City, United States, (2)University of Alabama, Alabama Water Institute, Tuscaloosa, United States, (3)University of Alabama, Cyber Institute, Tuscaloosa, United States, (4)University of Utah, Salt Lake City, United States, (5)University of Alabama, Alabama Water Institute, Tuscaloosa, AL, United States
 
Reservoir Inflow Forecasting Using a Hybrid Timeseries Decomposition-Convolutional Neural Network Model (1186982)
Mahdi Erfani, University of South Carolina Columbia, Columbia, SC, United States and Erfan Goharian, University of South Carolina, Civil and Environmental Engineering, Columbia, SC, United States
 
A Deep Learning approach for predicting River depth and Floodplain inundation (1187663)
Amobichukwu Amanambu1, Joann Mossa1 and Chen Yin-Hsuen Dr2, (1)University of Florida, Department of Geography, Gainesville, United States, (2)Old Dominion University, GeoSEA Center, Norfolk, United States
 
Predicting Salinity-driven Exchange Flow at the Great Salt Lake, UT using Machine-Learning in conjunction with Physics-based model (1188524)
Eric Larsen1, Som Dutta2, Brian Crookston1 and Andrew Crook3, (1)Utah State University, Logan, UT, United States, (2)Utah State University, Mechanical and Aerospace Engineering, Logan, United States, (3)Utah State University, Mechanical Engineering, Logan, United States
 
Regionalization of Sediment Rating Curves in Peninsular India using Clustering Approach (1188979)
Abhijith Sathya1 and Srinivas Venkata Vemavarapu1,2, (1)Indian Institute of Science, Department of Civil Engineering, Bangalore, India, (2)Indian Institute of Science, Interdisciplinary Centre for Water Research, Bangalore, India
 
Investigating River Ice Thickness and Analyzing Ice Breakup Flooding Events for Canadian Rivers (1189252)
Karl Grambow, University of Guelph, Guelph, ON, Canada, Bahram Gharabaghi, University of Guelph, School of Engineering, Guelph, ON, Canada and Jesse Van Griensven, President of Lakes Environmental Research, Waterloo, ON, Canada
 
AI-Based Streamflow Prediction in the Upper Colorado River Basin Using Multimodal Spatiotemporal Data (1189418)
Pouya Hosseinzadeh, Utah State University, Logan, United States, Ayman Nassar, Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States and Soukaina Filali Boubrahimi, Utah State University, Computer Science, Logan, United States
 
Deep Learning Short-term Heavy Rainfall Forecasting Using Pseudo Data (1189822)
Ryo Kaneko1, Shiho Onomura2 and Makoto Nakayoshi2, (1)Institute of Industrial Science, The University of Tokyo, Chiba, Japan, (2)Tokyo University of Science, Tokyo, Japan
 
ENSO Prediction in the Niño 3.4 and Niño 1+2 Regions, Using Deep Neural Networks with Space-Temporal Sequences (1193492)
Eusebio Ingol Blanco1, Kennedy Gomez Tunque Ing.1, Jesus Abel Mejia Marcacuzco Dr.1 and Daene C. McKinney2, (1)Graduate Program in Water Resources Engineering, Universidad Nacional Agraria La Molina, Lima, Peru, (2)University of Texas at Austin, Austin, United States
 
Super-Resolution from Space: Using MERRA-2 and MAIAC Satellite Imagery to Produce Daily Continuous 1 km PM2.5 Estimates (1195382)
Michael von Pohle, NASA Ames Research Center [USRA], Moffet Field, United States, Pawan Gupta, Universities Space Research Association, Huntsville, AL, United States, Meytar Sorek-Hamer, NASA Ames Research Center, USRA, Moffett Field, United States and Ata Akbari Asanjan, NASA Ames Research Center (USRA), Moffet Field, United States
 
Evaluating and intercomparing satellite and reanalysis precipitation products by using decision trees (1197818)
Xinxin Sui, University of Texas at Austin, Austin, TX, United States, Zhi Li, University of Oklahoma Norman Campus, Norman, OK, United States, Guoqiang Tang, University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada, Zong-Liang Yang, Univ Texas Austin, Austin, United States and Dev Niyogi, University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States
 
Daily Streamflow Prediction and Uncertainty Quantification Using Physics-informed Deep Learning Algorithms (1198237)
Sadegh Sadeghi Tabas, Clemson University, Department of Civil Engineering, Clemson, SC, United States and Vidya Samadi, Clemson University, Agricultural Sciences Department, Clemson, SC, United States
 
Streamflow Simulation via Explainable Deep Learning in Space and Time. (1198516)
Qi Deng, Tulane University of Louisiana, ByWater Institute, New Orleans, LA, United States, John L Sabo, Tulane University of Louisiana, ByWater Institute, New Orleans, United States, Yuhang Wei, Arizona State University, Future H2O, Julie Ann Wrigley Global Futures Laboratory, Tempe, United States and Hongkai Gao, East China Normal University, Shanghai, China
 
Climate Adaptation in 2021 and Beyond: Using Machine Learning and Earth Observation to Bolster Extreme Event Resilience (1198907)
Thomas Y Chen, Columbia University, New York, United States and Luca Marini, KTH Royal Institute of Technology, Stockholm, Sweden
 
A weather generator for the Lerma Chapala Basin using Machine Learning. (1200165)
Julian David Rojo Hernandez1, Alexandra Bruce2, Baptiste Francois1 and Casey Brown2, (1)University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, United States, (2)University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, MA, United States
 
The Global Freshwater System: Patterns and Predictability of Green-Blue Water flux Partitioning and Its Variations and Changes across Space and Time (1200305)
Georgia Destouni and Daniel Althoff, Stockholm University, Department of Physical Geography and the Bolin Centre for Climate Research, Stockholm, Sweden
 
The hitchhiker’s guide to differentiable modeling in hydrology. (1200501)
Chaopeng Shen, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States
 
Multimodel Multisite Classification and Regression Technique for the Statistical Downscaling of Temperature (1200755)
Asmita Naitam, Indian Institute of Technology, Tirupati, Tirupati, India and Meghana Nagaraj, PhD scholar, IIT Madras, Department of Civil Engineering, Chennai, India
 
Long Short-Term Memory (LSTM) Based Prediction of Standardized Snow Water Equivalent Index for Snow Drought Risk Assessment in the Pacific North-West (1201742)
Md Rubayet Mortuza, Washington State University, Department of Civil and Environmental Engineering, Pullman, WA, United States, MD Abul Ehsan Bhuiyan, NOAA/NWS/NCEP, Climate Prediction Center, College Park, MD, United States, Saranah Selmi, Washington State University, Department of Mathematics and Statistics, Pullman, United States and Yonas Demissie, Washington State University, Civil and Environmental Engineering, Pullman, WA, United States
 
Global-Scale AI for Gravity Prediction (1202428)
Dawn King1, Lexie Yang2, Dalton Lunga3, Marlie Mollett4, Clifton F Minter1, Jordan Bowman2, Jacob Arndt2, Robert Stewart2, Caitlin Glennon5, Max Wootten5, James Beale5 and Jason Wohlgemuth3, (1)National Geospatial Intelligence Agency, Springfield, VA, United States, (2)Oak Ridge National Laboratory, Geographic Data Science, Oak Ridge, TN, United States, (3)Oak Ridge National Laboratory, Geographic Data Science, Oak Ridge, United States, (4)University of Missouri St Louis and National Geospatial Intelligence Agency, St Louis, United States, (5)National Geospatial Intelligence Agency, St. Louis, United States
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