2023
DOI: 10.1016/j.geoderma.2023.116626
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Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains

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Cited by 6 publications
(1 citation statement)
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“…The analysis of random forest has implications for improving process‐based hydrological models that use topographic variables to parameterize subsurface water flow (Emanuel et al, 2010; Gao et al, 2018; Kang et al, 2023; Singh et al, 2018). The results of this study indicate that besides topographic variables, soil properties also play a crucial role in predicting the response of soil moisture to rainfall (Figure 10).…”
Section: Discussionmentioning
confidence: 99%
“…The analysis of random forest has implications for improving process‐based hydrological models that use topographic variables to parameterize subsurface water flow (Emanuel et al, 2010; Gao et al, 2018; Kang et al, 2023; Singh et al, 2018). The results of this study indicate that besides topographic variables, soil properties also play a crucial role in predicting the response of soil moisture to rainfall (Figure 10).…”
Section: Discussionmentioning
confidence: 99%