Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron neural network (MLPNN), support vector machine (SVM), multivariate adaptive regression splines (MARS), genetic programming (GP), M5 algorithm, and group method of data handling (GMDH) were used to predict the piezometric head in the core and the seepage discharge through the body of earth dam. For this purpose, the data recorded by the absolute instrument during the last 94 months of Shahid Kazemi Bukan Dam were used. The results showed that all of the applied models had a permissible level of accuracy in the prediction of the piezometric heads. The average error indices for the models in the training phase were R²= 0.957 and RMSE= 0.806 and in the testing phase were equal to R²= 0.949 and RMSE= 0.932, respectively. The performances of all models except the M5 and MARS in predicting seepage discharge are nearly identical; however, the best is the MARS, and the weakest is the M5 algorithm.

孔隙压力和渗流流量的预测是土坝设计中最重要的参数，也是在运行期间评估其安全性的最重要参数。在本研究中，软计算模型包括多层感知器神经网络 (MLPNN)、支持向量机 (SVM)、多元自适应回归样条 (MARS)、遗传规划 (GP)、M5 算法和数据处理组方法 (GMDH) ) 用于预测岩心中的测压头和通过土坝体的渗流排放。为此，使用了绝对值仪器在 Shahid Kazemi Bukan 大坝过去 94 个月内记录的数据。结果表明，所有应用的模型在预测测压头时都具有允许的准确度。² = 0.957 和 RMSE= 0.806，在测试阶段分别等于 R ² = 0.949 和 RMSE= 0.932。除 M5 和 MARS 外，所有模型在预测渗流排放方面的性能几乎相同；然而，最好的是MARS，最弱的是M5算法。