Researchers from Chinese Academy of Sciences Describe Findings in Machine Learni ng (A Novel Strategy for Automatic Selection of Cross-basin Data To Improve Local Machine Learning-based Runoff Models)

中国科学院的研究人员描述了机器学习(一种自动选择跨流域数据以改进基于机器学习的局部径流模型的新策略)的发现

Robotics & Machine Learning Daily News2024，Issue(Jun.14) ：5-6.

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Researchers from Chinese Academy of Sciences Describe Findings in Machine Learni ng (A Novel Strategy for Automatic Selection of Cross-basin Data To Improve Local Machine Learning-based Runoff Models)

中国科学院的研究人员描述了机器学习(一种自动选择跨流域数据以改进基于机器学习的局部径流模型的新策略)的发现

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摘要

由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在已经可用。根据NewsRx编辑在中国北京的新闻报道，研究表明：“以前的研究表明，区域深度学习(DL)模型可以利用大型水文数据集改进径流预测。但是，使用所有数据而不进行筛选训练DL区域模型可能会降低当地的性能。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting out of Beijing, People’s Republic of Chi na, by NewsRx editors, research stated, “Previous studies have shown that region al deep learning (DL) models can improve runoff prediction by leveraging large h ydrological datasets. However, training a DL regional model using all data witho ut screening may degrade local performance.”

Key words

Beijing/People’s Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Chinese Academy of Sciences

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出版年

2024

Robotics & Machine Learning Daily News

ISSN：

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