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

Pedotransfer function (PTFs) is widely developed using machine learning algorithms (MLA) and non-linear regression (NLR). Saturated hydraulic conductivity (Ks) is obviously used to regulate water movement in the soil. The objective of the work is to evaluate the accuracy of MLA to predict Ks. Moreover, is to determine the best class of predictors that can be used to predict Ks with little estimation error. Saturated hydraulic conductivity was measured by direct method using the constant head method. In addition, Ks was predicted using PTFs developed by machine three categories of soil physical predictors. Furthermore, PTFs developed by RFA, and GBA were compared with PTFs developed by NLR models using the same three categories of soil physical predictors. The three categories of soil physical predictors were the following: category-1 refers to sand, silt, and clay "SSC"; category-2 refers to category-1, in addition to bulk density "SSC+BD"; and category-3 refers to category-2, in addition to organic matter "SSC+BD+OM". The results of error analysis observed that PTFs developed by RFA were more accurate than GBA and NLR models, respectively, for Ks prediction with the three categories of soil physical predictors. Moreover, the category-3 which takes into account sand, silt, clay, bulk density, and organic matter was the best category used to predict Ks using RFA, and NLR models. PTFs developed by machine learning algorithms including RFA, and GBA models can be utilized to predict Ks with little calculation error. However, RFA was greater than GBA for calculating Ks. The efficiency of RFA was associated with the number of soil variables predictors and building classification and regression trees which is more robust than decision trees such as GBA to optimize Ks prediction.