Predicting Seepage of Earth Dams using Artificial Intelligence Techniques

The use of clay blanket in reservoirs is one of the main methods of seepage reducing. In this study, with clay blanket modeling in a proposed reservoir by finite element method, 350 dataset was obtained using SEEP/W. Validation of SEEP/W was carried out by comparing seepage results obtained from a laboratory tests. For evaluation of suitable model for predicting seepage values (results of modeling), used from five artificial intelligence techniques comprising: multilayer perceptron neural network (MLP), radial base function (RBF), gene expression programming (GEP), support vector regression (SVR) and a novel hybrid model of the firefly algorithm (FFA) with the multilayer perceptron (MLP-FFA). All the techniques were trained with 70% of available dataset and tested using the remaining 30% dataset. Different combinations of input data that include the ratio of the permeability coefficient of foundation to the permeability coefficient of clay blanket (K_f/K_b ), the ratio of the length of blanket to upstream head (L_1/H), the ratio of thickness of foundation to thickness of blanket (h_f/t), the ratio of length of blanket to thickness of core (L_1/L_2 ) and the ratio of horizontal to vertical permeability coefficient of foundation (K_(f_x )/K_(f_y ) ) were used for evaluation of mentioned methods. The results were evaluated using four performance criteria metrics: root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NS), Willmott’s Index of agreement (WI) and Taylor diagram. The results of study showed that the MLP-FFA method provides better estimation results than the other models and therefore, could be applied an optimized for predictive model of earth fill dam seepage.