The Slide2 model was used to estimate seepage losses from canals after validation considering different canal geometries, lining thicknesses, and lining materials. SPSS was used to develop three models: NLR, MLP-ANN, and RBF-ANN. MATLAB software was used to write down the script code for the ANNs. Results showed that seepage losses were highly increased when the liner had high hydraulic conductivity, while with the increase of lining thickness, a noticeable reduction in seepage losses was obtained. The canal's side slope had a minimal effect on the seepage losses. Moreover, the MLP-ANN and RBF-ANN models performed better than the NLR model with determination coefficient (R²) of 0.996 and 0.965; Root-Mean-Square-Error (RMSE) of 1.172 and 0.699; Mean-Absolute-Error (MAE) of 0.139 and 0.528; index of agreement (d) = 0.999 and 0.991, respectively. The NLR model had lower values of R² = 0.906, RMSE = 1.198, MAE = 0.942, and d = 0.971. Thus, ANNs are recommended as a robust, easy, simple, and rapid tool for estimating seepage losses from lined trapezoidal irrigation canals.

在考虑不同渠道几何形状、衬砌厚度和衬砌材料的验证后，使用 Slide2 模型来估计渠道的渗流损失。 SPSS 用于开发三个模型：NLR、MLP-ANN 和 RBF-ANN。 MATLAB 软件用于编写 ANN 的脚本代码。结果表明，当衬砌具有高导水率时，渗流损失大幅增加，而随着衬砌厚度的增加，渗流损失明显减少。运河边坡对渗漏损失的影响很小。此外，MLP-ANN和RBF-ANN模型的表现优于NLR模型，决定系数（R ²）分别为0.996和0.965；均方根误差 (RMSE) 分别为 1.172 和 0.699；平均绝对误差 (MAE) 为 0.139 和 0.528；一致性指数 ( d ) 分别 = 0.999 和 0.991。 NLR 模型的R ²  = 0.906、RMSE = 1.198、MAE = 0.942 和d  = 0.971的值较低。因此，人工神经网络被推荐作为一种强大、简单、简单和快速的工具，用于估计衬砌梯形灌溉渠的渗流损失。