Evaluation of pedotransfer functions to estimate saturated hydraulic conductivity using machine learning with random forest and gradient boosting algorithms

HIGHLIGHTS

• What: In this study, three categories of soil physical variables predictors were suggested to estimate Ks, it is shown in Table 2.
• Who: GEEKSTER from the Ain Shams University, Faculty of Agriculture, Department of Science, Cairo, Egypt have published the Article: 43(2): 268-277 Evaluation of pedotransfer functions to estimate saturated hydraulic conductivity using machine_learning with random forest and gradient boosting algorithms, in the Journal: (JOURNAL)

SUMMARY

In spite of the direct methods are accurate, however it is time-consuming and costly (Christiaens and Feyen, 2001; Islam et_al, 2006; Jorda . . .

 



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