Articles | Volume 27, issue 22
https://doi.org/10.5194/hess-27-4151-2023
https://doi.org/10.5194/hess-27-4151-2023
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

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Latest update: 17 Jul 2024
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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.