Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 22
Articles | Volume 27, issue 22
https://doi.org/10.5194/hess-27-4151-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-4151-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 22
Research article
 | 
16 Nov 2023
Research article |  | 16 Nov 2023

Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data

Daniel Camilo Roman Quintero, Pasquale Marino, Giovanni Francesco Santonastaso, and Roberto Greco

Viewed

Total article views: 944 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
686 209 49 944 32 34
  • HTML: 686
  • PDF: 209
  • XML: 49
  • Total: 944
  • BibTeX: 32
  • EndNote: 34
Views and downloads (calculated since 16 Jan 2023)
Cumulative views and downloads (calculated since 16 Jan 2023)

Viewed (geographical distribution)

Total article views: 944 (including HTML, PDF, and XML) Thereof 925 with geography defined and 19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 12 May 2024
Download
Short summary
This study shows a methodological approach using machine learning techniques to disentangle the relationships among the variables in a synthetic dataset to identify suitable variables that control the hydrologic response of the slopes. It has been found that not only is the rainfall responsible for the water accumulation in the slope; the ground conditions (soil water content and aquifer water level) also indicate the activation of natural slope drainage mechanisms.
Read more