# Soil and Rockfill Dams Safety Assessment for Henan Province: Monitoring, Analysis and Prediction
## 5 Discussion
### Results and Accuracy Evaluation of MAF-SSA-LSTM Model  
The MAF-SSA-LSTM model was optimized using the Adam optimizer in this experiment. The sparrow population size was fixed at 20, with a population dimension of 3. The initial learning rate was set within the range of [1 \(\times\) 10\({}^{-3}\), 1 \(\times\) 10\({}^{-2}\)], while the regularization coefficient and the number of nodes in the hidden layer were limited to [1 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-1}\)] and [10; 30], respectively. The model was trained for a maximum of 8 iterations, undergoing a total of 1000 training cycles. Following the experimental process above, the deformation data of Xiaolangdi Dam was analyzed using the MAF-SSA-LSTM model. The prediction effect was visualized in Figure 17a,b. Meanwhile, the prediction results of different models are displayed in Figure 17c. Figure 17a shows a scatter plot of the true and predicted deformation data as represented by the blue line. It is observed that the two are closely aligned and are roughly  
Figure 17: (**a**) Correlation between predicted value and original value of MAF-SSA-LSTM model; (**b**) prediction error curve of different buffers; (**c**) comparison of prediction results of three network models. on the same line with only slight fluctuations. There is a good correlation between the predicted value and the original value. Figure 17b represents the prediction error curve of different buffers. The maximum prediction error is 2.61 mm. As the number of predicted samples increases, the prediction error gradually decreases, and the minimum prediction error is 0.07 mm. Meanwhile, because this study uses the dataset split ratio of 80% training and 20% test, each sample represents 12 days (satellite repetition time). Therefore, 33 samples were used for prediction. The forecast time is about 396 days. To further prove the accuracy of the results of the MAF-SSA-LSTM model, four buffers of the same size were selected at an equal distance in the middle of the dam. The deformation was predicted using the MAF-SSA-LSTM model. The prediction error values of different regions were calculated, and we obtained the error between the predicted deformation and the real deformation in the four regions. RMSE, MAE, and MAPE were calculated. The prediction error of multiple buffers showed that the prediction error was gradually decreasing with the end of the prediction. The accuracy evaluation values of each region are less than 3% of the total error. It has been proven that the model has good reliability and accuracy. Figure 17c represents the prediction results of three network models. The Sparrow Search Algorithm Long Short-Term Memory (SSA-LSTM) model and the Error Back Propagation (BP) model are traditional models used for deformation prediction. Select the same dataset to compare the accuracy of the three models. The red line represents PS-InSAR monitoring results, and different color curves represent the prediction results of different models. It can be seen from the figure that the deformation prediction using the MAF-SSA-LSTM model has good reliability. Compared with the other two neural networks, RMSE decreased by 0.145 and 0.778. Compared with the SSA-LSTM model, the optimized network model has a trend closer to the real monitoring data in maintaining a low prediction error. Compared with the BP algorithm, the prediction error of the optimized network model is improved, and the prediction accuracy is significantly improved. The MAF-SSA-LSTM model shows great advantages. The results indicate that the MAF-SSA-LSTM model used in this study can approximate the trend of the PS-InSAR monitoring curve. To intuitively analyze the prediction performance, three regression prediction metrics were selected to evaluate the performance of the MAF-SSA-LSTM model's prediction algorithm. These metrics include Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) [51].