首页|Investigators at University of Utah Report Findings in Machine Learning (Quantif ying Regional Variability of Machine-learningbased Snow Water Equivalent Estima tes Across the Western United States)
Investigators at University of Utah Report Findings in Machine Learning (Quantif ying Regional Variability of Machine-learningbased Snow Water Equivalent Estima tes Across the Western United States)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Salt Lake City, Utah, by NewsRx journalists, research stated, "Seasonal snow -derived water is a crit ical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow -water -equ ivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water manag ement efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging pu blicly available data sources and open -source software." Funders for this research include Cooperative Institute for Research to Operatio ns in Hydrology (CIROH) through the NOAA Cooperative, University of Alabama, Uni ted States.
Salt Lake CityUtahUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniver sity of Utah
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