Saturated hydraulic conductivity (K_s) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting K_s. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases.

饱和导水率 (K _s ) 是控制水在土壤中移动的重要土壤特性。另一方面，其测量困难、耗时且昂贵；因此，Pedotransfer Functions (PTFs) 通常用于其估计。尽管多年来取得了重大进展，但 PTF 在预测 K _{s方面表现不佳}. 本研究使用遗传算法（GA），提出了两种基于机器学习的混合型PTF（ML-PTF），即遗传算法与多层感知器（MLP-GA）和支持向量机（SVM-GA）的组合。我们比较了四种机器学习算法对不同预测变量集的性能。包含沙子、粘土、田间容量和枯萎点的预测器组合显示出所有 ML-PTF 的最高准确度。在 ML-PTF 中，SVM-GA 算法优于其他 PTF。值得注意的是，SVM-GA PTF 表现出比 MLP-GA 算法更高的效率。水力传导率预测的参考模型被选为与 K-5 预测变量配对的 SVM-GA PTF。将提议的 PTF 与过去文献中的 160 个模型进行了比较。发现所提倡的算法是对这些 PTF 的改进。当前模型将有助于使用预先可用的数据库对水力传导率进行有效的时空测量。