首页|Findings from Desert Research Institute Yields New Data on Machine Learning (Imp roving Crop-specific Groundwater Use Estimation In the Mississippi Alluvial Plai n: Implications for Integrated Remote Sensing and Machine Learning Approaches In ...)
Findings from Desert Research Institute Yields New Data on Machine Learning (Imp roving Crop-specific Groundwater Use Estimation In the Mississippi Alluvial Plai n: Implications for Integrated Remote Sensing and Machine Learning Approaches In ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating from Reno,Neva da,by NewsRx correspondents,research stated,"Study region: The Mississippi Al luvial Plain (MAP) in the United States (US). Study focus: Understanding local-s cale groundwater use,a critical component of the water budget,is necessary for implementing sustainable water management practices." Funders for this research include United States Department of Agriculture (USDA) ,University of Colorado Boulder,University of Colorado Anschutz,Colorado Stat e University,National Science Foundation (NSF). Our news editors obtained a quote from the research from Desert Research Institu te,"The MAP is one of the most productive agricultural regions in the US and ex tracts more than 11 km3/year for irrigation activities. Consequently,groundwate r-level declines in the MAP region pose a substantial challenge to water sustain ability,and hence,we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region: W e incorporate remote sensing datasets and machine learning to improve an existin g lookup table-based model of groundwater use previously developed by the U.S. G eological Survey (USGS). Here,we employ Distributed Random Forests,an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014- 2020) throughout this region at 1-km resolution,using pumping data from existin g flowmeters in the Mississippi Delta. Our model compares favorably with the exi sting USGS model,with higher R2 (0.51 compared to 0.42 in the previous model),and lower root mean square error (RMSE) and mean absolute error (MAE)- 0.14 m an d 0.09 m,respectively in our model,compared to 0.15 m and 0.1 m in the previou s model."
RenoNevadaUnited StatesNorth and C entral AmericaCyborgsEmerging TechnologiesMachine LearningRemote SensingDesert Research Institute
NETL
NSTL
