The infiltration rate is one of the primary processes of the hydrological cycle. It is the property of water by which it moves through the soil particles. Good knowledge of the infiltration rate is useful in calculating the natural and artificial groundwater recharge, soil erosion, and surface runoff. In this study, actual field measurements such as bulk density (B), water content (W_c), percentage of sand (Sa), silt (Si) and clay (C), and time (T) were used by five data-driven models to predict the infiltration rate of the soil. These data-driven models are multi-linear regression (MLR), neural network (NN), M5P model tree (M5P), random forest regression (RF), and Gaussian process (GP). The study area is situated in the Islamic Republic of Iran. The dataset contains 155 experimental measurements of the infiltration rate, which was collected using a double-ring infiltrometer. Out of 155 experimental observations arbitrarily chosen, 105 measurements were selected for training, whereas residual 50 were selected for testing the models. Three statistical parameters, root mean square error (RMSE), Nash-Sutcliffe model efficiency (NSE), and coefficient of correlation (C.C), were selected to compare the efficiency of all models. All data-driven models are capable of predicting the infiltration rate precisely. The comparative analysis of result suggests that the NN has very high performance with C.C = 0.9300, followed by GP, RF, M5P, and MLR (C.C = 0.9022, 0.8844, 0.6873, and 0.6016, respectively). Also, a comparison is made with the past studies, which indicated the NN model is best to predict the infiltration rate. Finally, the results revealed that time is the most important parameter for estimating the infiltration rate.

 入渗率是水文循环的主要过程之一。它是水的属性，它通过土壤颗粒流动。对渗透率的充分了解有助于计算天然和人工地下水的补给量，土壤侵蚀和地表径流。在这项研究中，实际的野外测量，例如堆积密度（B），含水量（W _c），五个数据驱动模型使用了沙（Sa），粉砂（Si）和粘土（C）的百分比以及时间（T）来预测土壤的入渗率。这些数据驱动的模型是多线性回归（MLR），神经网络（NN），M5P模型树（M5P），随机森林回归（RF）和高斯过程（GP）。研究区域位于伊朗伊斯兰共和国。数据集包含155个渗透率的实验测量值，这些测量值是使用双环渗透仪收集的。在任意选择的155个实验观察值中，选择了105个测量值进行训练，而其余50个则被选择用于测试模型。选择三个统计参数，即均方根误差（RMSE），Nash-Sutcliffe模型效率（NSE）和相关系数（CC），以比较所有模型的效率。所有数据驱动的模型都能够准确预测渗透率。结果的比较分析表明，NN具有很高的性能，CC = 0.9300，其次是GP，RF，M5P和MLR（分别为CC = 0.9022、0.8844、0.6873和0.6016）。此外，与以往的研究进行了比较，这表明NN模型最能预测渗透率。最后，结果表明时间是估计渗透率的最重要参数。这表明NN模型最适合预测渗透率。最后，结果表明时间是估计渗透率的最重要参数。这表明NN模型最适合预测渗透率。最后，结果表明时间是估计渗透率的最重要参数。