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Manuscript.pdf

hess-2022-63-manuscript-version4.pdf
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🔗 Source: hess.copernicus.org
📊 Size: 4.96 MB
👤 Author: Windows User
⬇️ Downloads: 78

Summary

Researchers integrated process-related information into artificial neural networks (ANNs) to predict root-zone soil moisture (RZSM), a crucial parameter for agricultural applications and soil sciences. They combined surface soil moisture information with features like infiltration process, evaporation efficiency, and vegetation growth, and trained the ANN models using in situ stations from the International Soil Moisture Network (ISMN). The results showed that integrating process-related features improved the overall performance of the ANN models, particularly in arid and semi-arid areas, with significant enhancements in prediction accuracy when evaporation efficiency was included. However, the added value of process-related features was limited in temperate to tropical conditions.

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Description

Researchers integrated process-related information into artificial neural networks (ANNs) to predict root-zone soil moisture (RZSM), a crucial parameter for...

Technical Information

  • File Format: PDF
  • File Size: 4.96 MB
  • Pages: 36
  • Language: EN
  • Author: Windows User
  • Total Downloads: 78
  • Last Updated: 1 week ago

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