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: Leveraging Large-Scale Meteorological Geospatial Information to Predict Snow Water Equivalent with Machine Learning (AGU25)

Leveraging Large-Scale Meteorological Geospatial Information to Predict Snow Water Equivalent with Machine Learning

Savalan Naser Neisary, The University of Alabama, Department of Civil, Construction, and Environmental Engineering, Tuscaloosa, United States and Jonathan M Frame, University of Alabama, Department of Geological Sciences, Tuscaloosa, AL, United States
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