The deadline to submit a session, town hall, or pre-conference workshop proposal for AGU25 has passed. Please search for and review proposed sessions below. Please note this site is frozen as of 4/29/25 and will not reflect any changes to proposals made after this date.


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Abstract: Deep Learning-Based Prediction of GRACE-Derived Terrestrial Water Storage Anomalies in the Nile Basin (2003–2024) Using a CNN-LSTM Approach (AGU25)

Deep Learning-Based Prediction of GRACE-Derived Terrestrial Water Storage Anomalies in the Nile Basin (2003–2024) Using a CNN-LSTM Approach

Bewuket Tefera, University of Florida, Geography Department, Gainesville, United States, Jane Southworth, University of Florida, Department of Geography, Gainesville, United States, Mohammad Safaei, University of Florida, Gainesville, FL, UNITED STATES and Nafyad Serre Kawo, Moore engineering Inc, Hydrogeophysicist, Lakewood, United States
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