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Updated: Jun 24, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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我们如何量化,解释和应用神经网络预测的复杂土壤地图的不确定性?

Kerstin Rau1, Katharina Eggensperger2, Frank Schneider3

  • 1Department of Geoscience, University of Tübingen, Rümelinstraße 19-23, Tübingen 72070, Baden-Württemberg, Germany; Cluster of Excellence Machine Learning: New Perspectives for Sciene, University of Tübingen, Maria-von-Linden-Straße 6, Tübingen 72076, Baden-Württemberg, Germany; Tübingen AI Center, Maria-von-Linden-Straße 6, Tübingen 72076, Baden-Württemberg, Germany.

The Science of the total environment
|June 12, 2024
PubMed
概括

人工神经网络 (ANN) 可以预测土壤地图,但往往缺乏不确定性量化. 贝叶斯深度学习通过提供现实的不确定性来改善ANN,提高了土壤分类可靠性.

关键词:
数字土壤绘制地图最后一个层的拉普拉斯近似.模型的不确定性土壤类型 土壤类型空间预测的空间预测

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