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Filling GRACE data gap using an innovative transformer-based deep learning approach

Created2024-12-27|Updated2025-02-14
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Author: ALTNT
Link: http://blog.705553939.xyz/2024/12/27/crop_classification/Crop%20classification/2024-RSE-Filling_Grace_data_gap/
Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 4.0 unless stating additionally.
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Contents
  1. 1. Filling GRACE data gap using an innovative transformer-based deep learning approach
    1. 1.1. 摘要
    2. 1.2. 1 引言
    3. 1.3. 2 数据
      1. 1.3.1. 2.1. GRACE data
      2. 1.3.2. 2.2. Climate data
      3. 1.3.3. 2.3. Evapotranspiration data
    4. 1.4. 3、方法
      1. 1.4.1. 3.1. Transformer
      2. 1.4.2. 3.2. 训练及验证指标与策略
      3. 1.4.3. 3.3 性能指标
        1. 1.4.3.1. (Kruskal-Wallis检验)
          1. 1.4.3.1.1. 原理
          2. 1.4.3.1.2. 优点
          3. 1.4.3.1.3. 缺点
          4. 1.4.3.1.4. 应用场景
    5. 1.5. 4、结果
      1. 1.5.1. 4.1填补空缺结果的验证
      2. 1.5.2. 4.2不同干旱条件下的准确性评估
      3. 1.5.3. 4.3时间序列中填补空缺质量的评估——这个跟 4.1 节的区别是什么?
    6. 1.6. 5、讨论
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