WE4.R9.4
PHYSICS-INFORMED GEO-AI FOR QUANTIFYING IRRIGATION WITHDRAWALS: APPLICATION TO THE CENTRAL VALLEY
Esmaeel Adrah, Kent State University; Luca Brocca, National Research Council; Manzhu Yu, Penn State University; He Yin, Kent State University
Session:
WE4.R9: Physics-Informed Machine Learning in Remote Sensing (4/4) Oral
Track:
Community Contributed Themes
Location:
Fairchild
Presentation Time:
Wednesday, 12 August, 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
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; Zhengwei Yang, Yanbo Huang, USDA; Rajat Bindlish, NASA; Zhou Zhang, University of Wisconsin–Madison
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; Pingping Xie, Janice Bytheway, NOAA
WE4.R9.4: PHYSICS-INFORMED GEO-AI FOR QUANTIFYING IRRIGATION WITHDRAWALS: APPLICATION TO THE CENTRAL VALLEY
Esmaeel Adrah, Kent State University; Luca Brocca, National Research Council; Manzhu Yu, Penn State University; He Yin, Kent State University
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; Tony Bush, Noise Consultants; Sei Cabrol, Alain Retière, Everimpact
Resources
No resources available.