Determination of wetting patterns’ dimensions is essential in designing and managing surface/subsurface drip irrigation systems. The laboratory experiments were conducted using physical model with dimensions of 3 × 1 × 0.5 m³ to evaluate the moisture redistribution process under continuous and pulse surface/subsurface irrigation systems. In the present study, the efficiency of a new machine learning method, named fuzzy c-means clustering- based adaptive neural-fuzzy inference system combined with a new meta-heuristic algorithm, hybrid particle swarm optimization – gravity search algorithm (ANFIS-FCM-PSOGSA), is investigated in order to model wetting front redistribution of drip irrigation systems (IS) using soil and system parameters as inputs under continuous and pulse surface/subsurface IS. The outcomes of the assessed method are compared with those of the ANFIS-FCM-PSO, generalized regression neural networks and multivariate adaptive regression splines. In assessing the implemented methods, four commonly used indices, root mean square errors (RMSE), mean absolute error (MAE), coefficient of determination (R²), Nash-Sutcliffe model efficiency (NSE) and graphical methods (e.g., scatter, box plot and Taylor diagrams) are utilized. The benchmark outcomes demonstrate the superiority of new method in estimating wetting front dimensions by improving the accuracy of the ANFIS-FCM-PSO by 29.6%, 18.5%, 6.1%, and 9.0% in estimating the diameter of horizontal redistribution with respect to RMSE, MAE, R² and NSE, respectively. Furthermore, the ANFIS-FCM-PSOGSA respectively improves the RMSE, MAE, R² and NSE accuracy of the ANFIS-FCM-PSO by 20.1%, 19.2%, 35.7% and 35.6% in estimating the diameter of downward vertical redistribution. The general outcomes recommend the use of new method in estimating wetting front dimensions of drip irrigation systems.

确定润湿模式的尺寸对于设计和管理地表/地下滴灌系统至关重要。实验室实验使用尺寸为 3 × 1 × 0.5 m ^{3 的}物理模型进行评估连续和脉冲地表/地下灌溉系统下的水分再分配过程。在本研究中，一种新的机器学习方法的效率，称为基于模糊 c 均值聚类的自适应神经模糊推理系统，结合新的元启发式算法，混合粒子群优化 - 重力搜索算法（ANFIS-FCM- PSOGSA), 以模拟滴灌系统 (IS) 的润湿前沿再分布，使用土壤和系统参数作为连续和脉冲表面/地下 IS 下的输入。将评估方法的结果与 ANFIS-FCM-PSO、广义回归神经网络和多元自适应回归样条的结果进行比较。在评估实施方法时，四个常用指标，均方根误差（RMSE），² )、纳什-萨特克利夫模型效率 (NSE) 和图形方法（例如，散点图、箱线图和泰勒图）被利用。基准结果通过将 ANFIS-FCM-PSO 在估计水平再分布直径相对于 RMSE 的精度提高 29.6%、18.5%、6.1% 和 9.0%，证明了新方法在估计润湿前沿尺寸方面的优越性，分别为 MAE、R ²和 NSE。此外，ANFIS-FCM-PSOGSA在估计向下垂直重新分布的直径时分别将ANFIS-FCM-PSO的RMSE、MAE、R ²和NSE精度提高了20.1%、19.2%、35.7%和35.6%。一般结果建议使用新方法估算滴灌系统的湿润前沿尺寸。