首页|Study Results from University of Cincinnati Provide New Insights into Machine Le arning (Evaluation of Hydrological Models At Gauged and Ungauged Basins Using Ma chine Learning-based Limits-of-acceptability and Hydrological Signatures)

Study Results from University of Cincinnati Provide New Insights into Machine Le arning (Evaluation of Hydrological Models At Gauged and Ungauged Basins Using Ma chine Learning-based Limits-of-acceptability and Hydrological Signatures)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting out of Cincinnati, Ohio, by NewsRx editors, research stated, “Hydrological models are evaluated by comparisons with observed hydrological quantities such as streamflow. A model evaluation procedu re should account for dominantly epistemic errors in hydrological data such as m odel input precipitation and streamflow and avoid type-2 errors (rejecting a goo d model).” Financial supporters for this research include Institute Project Assignment fund s of Desert Research Institute, United States Environmental Protection Agency.

CincinnatiOhioUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Cincinnati

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Sep.18)