Studies from University of Connecticut Have Provided New Data on Machine Learnin g (Assessing Physical and Biological Lake Oxygen Indicators Using Simulated Envi ronmental Variables and Machine Learning Algorithms)
Studies from University of Connecticut Have Provided New Data on Machine Learnin g (Assessing Physical and Biological Lake Oxygen Indicators Using Simulated Envi ronmental Variables and Machine Learning Algorithms)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting out of Storrs,Connecticut, by NewsRx editors, research stated, "We integrate observations and simulated data from physics-base d models with observations and machine learning (ML) algorithms to assess and pr edict lake dissolved oxygen (DO) and Apparent Oxygen Utilization (AOU). DO is a proxy of hypoxia, and AOU a proxy of respiration processes and biological activi ty." Financial support for this research came from Department of Education's Graduate Assistantships in Areas of National Need (GAANN) project "Environmental Enginee ring at the Forefront of Water Science, Policy and Education."
Key words
Storrs/Connecticut/United States/Nort h and Central America/Algorithms/Chalcogens/Cyborgs/Emerging Technologies/M achine Learning/University of Connecticut
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