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    <identifier>10.57760/sciencedb.13697</identifier>
    <datestamp>2024-09-25T14:30:27Z</datestamp>
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  <dc:date>2024-09-25</dc:date>
  <dc:title>Improving time upscaling of instantaneous evapotranspiration based on machine learning models</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.13697</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This dataset is derived from the paper &amp;quot;Improved Time Adjustment of Instantaneous Evapotranspiration Based on Machine Learning Models.&amp;quot; It contains results of instantaneous evapotranspiration (ET) for training and evaluating models. The dataset includes two parts:Result data: ConDF.csv and ConEF+CorEF+ML.csvModel code: code.docx.These data can be used for further research and applications, aiming to enhance the estimation accuracy of instantaneous evapotranspiration and its potential applications in environmental science.</dc:description>
  <dc:subject>evapotranspiration; machine learning; dataset</dc:subject>
  <dc:creator>Danni Yang</dc:creator>
  <dc:creator>Shanshan Yang</dc:creator>
  <dc:creator>Huang Jiaojiao</dc:creator>
  <dc:creator>Shuyu Zhang</dc:creator>
  <dc:creator>Sha Zhang</dc:creator>
  <dc:creator>Zhang JiaHua</dc:creator>
  <dc:creator>Bai Yun</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://api.github.com/licenses/mit</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1080/20964471.2024.2423431</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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