In this study, novel methods such as wavelet–artificial neural network hybrid models and artificial neural network models were used to predict seepage from the Zonouz earth dam. The dataset consisted of 972 piezometric data points. Statistical fitting methods such as root mean squared error, determination coefficient, scatter plots, and data distribution diagrams were used to evaluate the results. The findings indicated that the wavelet–artificial neural network hybrid model was more accurate than the artificial neural network model. Specifically, during training, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.820, 0.680, 743.39, and 792.52, while the artificial neural network model had 0.700, 0.600, 426.39, and 131.45. Similarly, during validation, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.700, 0.600, 426.39, and 131.45, while the artificial neural network model had 0.823, 0.680, 743.39, and 792.52. Therefore, the wavelet–artificial neural network hybrid model can be proposed as a precise method for predicting seepage in earth dams and is more accurate than the artificial neural network model. This study highlights the importance of preventing dam failures and using advanced modeling techniques for better predictions and preventive measures.

  • This study focuses on predicting seepage from the Zonouz earth dam using novel methods.

  • Statistical fitting methods, including root mean square error, determination coefficient, scatter plots, and data distribution diagrams, were employed.

  • This study emphasizes the significance of preventing dam failures and underscores the use of advanced modeling techniques.

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