The GA-DBN model in this dataset is developed based on Python and contains the model code and parameter datasets. 193 groups of 8 levee leakage impact factors and 73 groups of 5 levee impact factors of Yangxin dry dyke defenses are derived from the corresponding actual datasets, which are sorted out and standardized to provide reliable inputs for model training and prediction. Data. These data are of high practical value to support the risk assessment, prediction analysis and model optimization of levee leakage.

  The data processing process starts with model tuning and data normalization. In the tuning stage, the model performance is optimized by adjusting the hyperparameters (e.g., learning rate, number of layers, etc.) of the GA-DBN model to ensure the accuracy of the training results.

  In the data normalization stage, all the influence factor data are processed by standardization or normalization methods to eliminate the bias caused by different data scales and ensure that the factors are compared and trained at the same scale. After these preprocessing steps, the dataset provides more consistent and stable inputs for the training and prediction of the GA-DBN model, which helps to improve the accuracy and generalization ability of the model.

  The dataset was rigorously processed to ensure data integrity and consistency. During the training and validation of the GA-DBN model, the model achieved 100% accuracy, recall, precision and F1 score, indicating that the dataset is of extremely high quality. These excellent performance metrics not only validate the accuracy and reliability of the data, but also indicate that the dataset can effectively support the practical application of embankment leakage prediction with high practical value. The excellent performance of the model further proves the effectiveness and accuracy of the data in dealing with embankment leakage risk analysis.