Robotics & Machine Learning Daily News2024,Issue(Apr.23) :15-16.

Investigators at U.S. Department of Agriculture (USDA) Report Findings in Machin e Learning (Leveraging Next-generation Satellite Remote Sensing-based Snow Data To Improve Seasonal Water Supply Predictions In a Practical Machine Learning-dri ven ...)

Robotics & Machine Learning Daily News2024,Issue(Apr.23) :15-16.

Investigators at U.S. Department of Agriculture (USDA) Report Findings in Machin e Learning (Leveraging Next-generation Satellite Remote Sensing-based Snow Data To Improve Seasonal Water Supply Predictions In a Practical Machine Learning-dri ven ...)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According tonews reporting originating from Port land, Oregon, by NewsRx correspondents, research stated, “Seasonalpredictions o f spring-summer river flow volume (water supply forecasts, WSFs) are foundationa lto western US water management. We test a new space-based remote sensing produ ct, spatially andtemporally complete (STC) MODSCAG fractional snow-covered area (fSCA), as input for the Natural ResourcesConservation Service (NRCS) operatio nal US West-wide WSF system. fSCA data were consideredalongside traditional SNO TEL predictors, in both statistical and AI-based NRCS operational hydrologicmod els, throughout the forecast season, in four test watersheds (Walker, Wind, Pied ra, and Gila Riversin California, Wyoming, Colorado, and New Mexico).”

Key words

Portland/Oregon/United States/North a nd Central America/Cyborgs/Emerging Technologies/Machine Learning/Remote Sen sing/U.S. Department of Agriculture (USDA)

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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