The infiltration process during irrigation is an essential variable for better water management and hence there is a need to develop an accurate model to estimate the amount infiltration water during irrigation. However, the fact that the infiltration process is a highly non-linear procedure and hence required special modeling approach to accurately mimic the infiltration procedure. Therefore, the ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated water during irrigation in the furrow for sustainable management is proposed. The main innovation of current research is the first attempt to employ the ANFIS model for predicating infiltration rates, in addition, integrate the ANFIS model with three new optimization algorithms. Three optimizing algorithms, viz. Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) were used to tune the ANFIS-parameters. Experimental data from six different studies in different countries have been used in this study to validate the proposed model. The inflow rate, furrow length, infiltration opportunity time, cross-sectional area, and waterfront advance time have been utilized as the input parameters. The results indicated that the ANFIS-SCA could provide a better estimation for the infiltration rate compared to ANFIS-PSO. The Mean Absolute Error (MAE) and Percent Bias (PBIAS) errors computed for the ANIFS-SCA (0.007 m³/m and 0.12) was significantly better than those achieved from the ANFIS-FFA and the ANFIS-PSO In addition to that, ANIFS-SCA model outperformed ANFIS-FFA with high level of accuracy. The proposed Hybrid ANFIS-SCA showed outstanding performance over the other optimizer algorithms in estimating the infiltration rate and could be applied in different irrigation systems for better sustainable irrigation management.

灌溉过程中的入渗过程是更好的水管理的重要变量，因此需要开发一个准确的模型来估计灌溉过程中的入渗水量。然而，渗透过程是一个高度非线性的过程，因此需要特殊的建模方法来准确模拟渗透过程。因此，提出了自适应神经模糊接口系统 (ANFIS) 模型在估计犁沟灌溉过程中渗透水的能力，以实现可持续管理。当前研究的主要创新是首次尝试采用ANFIS模型预测渗透率，并将ANFIS模型与三种新的优化算法相结合。三种优化算法，即。正余弦算法（SCA），粒子群优化 (PSO) 和萤火虫算法 (FFA) 用于调整 ANFIS 参数。本研究使用了来自不同国家的六项不同研究的实验数据来验证所提出的模型。入流速率、沟长、入渗机会时间、横截面积和滨水推进时间已被用作输入参数。结果表明，与 ANFIS-PSO 相比，ANFIS-SCA 可以更好地估计渗透率。为 ANIFS-SCA 计算的平均绝对误差 (MAE) 和百分比偏差 (PBIAS) (0.007 m 入渗机会时间、横截面积和滨水推进时间已被用作输入参数。结果表明，与 ANFIS-PSO 相比，ANFIS-SCA 可以更好地估计渗透率。为 ANIFS-SCA 计算的平均绝对误差 (MAE) 和百分比偏差 (PBIAS) (0.007 m 入渗机会时间、横截面积和滨水推进时间已被用作输入参数。结果表明，与 ANFIS-PSO 相比，ANFIS-SCA 可以更好地估计渗透率。为 ANIFS-SCA 计算的平均绝对误差 (MAE) 和百分比偏差 (PBIAS) (0.007 m³ /m 和 0.12) 显着优于 ANFIS-FFA 和 ANFIS-PSO 所实现的结果除此之外，ANIFS-SCA 模型在高精度水平上优于 ANFIS-FFA。所提出的混合 ANFIS-SCA 在估计渗透率方面表现出优于其他优化器算法的性能，可应用于不同的灌溉系统，以实现更好的可持续灌溉管理。