WE4.R9.4
PHYSICS-INFORMED GEO-AI FOR QUANTIFYING IRRIGATION WITHDRAWALS: APPLICATION TO THE CENTRAL VALLEY
Esmaeel Adrah, Kent State University, United States; Luca Brocca, National Research Council, Italy; Manzhu Yu, Penn State University, United States; He Yin, Kent State University, United States
Session:
WE4.R9: Physics-Informed Machine Learning in Remote Sensing (4/4) Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Wed, 12 Aug, 17:00 - 17:15
Session Co-Chairs:
Davide De Santis, and Grigorios Tsagkatakis,
Presentation
Discussion
Resources
No resources available.
Session WE4.R9
WE4.R9.1: Drought-induced changes in groundwater-surface water exchange at Lake Mead area
Mohammad Khorrami, Susanna Werth, Sonia Zehsaz, Manoochehr Shirzaei, Virginia Tech, United States
WE4.R9.2: KGML-SM: knowledge-guided machine learning with soil moisture for drought-aware corn yield prediction
Xiaoyu Wang, Yijia Xu, Jingyi Huang, University of Wisconsin-Madison, United States; Zhengwei Yang, Yanbo Huang, USDA, United States; Rajat Bindlish, NASA, United States; Zhou Zhang, University of Wisconsin–Madison, United States
WE4.R9.3: A TRANSFORMER-BASED DEEP LEARNING MODEL FOR PRECIPITATION RETRIEVALS USING ATMS OBSERVATIONS ABOARD THE NOAA/JPSS SATELLITES
Liping Wang, Haonan Chen, Colorado State University, United States; Pingping Xie, Janice Bytheway, NOAA, United States
WE4.R9.4: PHYSICS-INFORMED GEO-AI FOR QUANTIFYING IRRIGATION WITHDRAWALS: APPLICATION TO THE CENTRAL VALLEY
Esmaeel Adrah, Kent State University, United States; Luca Brocca, National Research Council, Italy; Manzhu Yu, Penn State University, United States; He Yin, Kent State University, United States
WE4.R9.5: PHYSICS-AWARE AI SURROGATE FOR CFD RETROPLUME INVERSION: FAST CITY-SCALE CO2 EMISSION MAPPING FROM SPARSE IOT AND EO INPUTS
Rabeb Naassaoui, David Petit, Metaplanet, France; Tony Bush, Noise Consultants, United Kingdom; Sei Cabrol, Alain Retière, Everimpact, France
Contacts