首页|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 ...)
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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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).”
PortlandOregonUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningRemote Sen singU.S. Department of Agriculture (USDA)
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