# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 3 Results and Discussion
In order to illustrate the prediction effect of each model more intuitively, six evaluation indicators are selected, the corresponding values are shown in Table 5, among which proposed model is selected as the comparison model. Table 5 shows that the model proposed in this study is the lowest in all comparison models, whether it is MAE or RMSE, and its MAE and RMSE values are 0.174 and 0.214, respectively. The correlation coefficient between the real value and the predicted value is the closest to 1, but due to the denseness of the data, the correlation coefficient is increased by 2.2% at the minimum and 9.6% at the maximum. It can be seen from the table that  
Figure 9: Measurement point EX5 high and low frequency prediction and correlation coefficient. ((**a**) High frequency prediction; (**b**) Linear regression analysis of LSTM; (**c**) Low frequency prediction; (**d**) Linear regression analysis of RF). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MAE** & **RMSE** & \(R^{2}\) & \(P_{MAE}\) (\%) & \(P_{RMSE}\) (\%) & \(P_{R^{2}}\) (\%) \\ \hline Proposed & 0.174 & 0.214 & 0.993 & / & / & / \\ Model & 0.402 & 0.512 & 0.972 & 56.7 & 58.2 & 2.2 \\ RF & 0.355 & 0.441 & 0.969 & 51.0 & 51.5 & 2.5 \\ ELM & 0.641 & 0.764 & 0.906 & 72.9 & 72.0 & 9.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of evaluation indexes of measuring point EX5. the prediction effect of a single LSTM model and RF model on the overall data is not as good as the proposed model. The MAE and RMSE of a single LSTM are 0.402 and 0.512, respectively. The MAE and RMSE values of a single RF are 0.355 and 0.441, respectively. Based on LSTM, the proposed model has increased the corresponding MAE and RMSE values by 56.7% and 58.2%, respectively. It also increased by 51.0% and 51.5%, respectively, on the basis of RF. Compared with ELM, the improved performance is more significant, reaching 72.9% and 72.0%. In order to further verify the validity of the model, the model is then applied to the deformation data of the EX2 measuring point, and it was also compared with the other three models. The prediction results are shown in Figure 12 and Table 6.  
Through the prediction results of each model at the EX2 measurement point, it can be found that the proposed model is still the closest to the true value, as shown in Figure 12a. Figure 12b shows that the residual is the smallest and most stable. Figure 12c exhibits that there are almost no extreme outliers between the observed value and the predicted value, which shows that the proposed model improves the performance of a single LSTM and RF to a certain extent. According to Table 6, it can be seen that the degree of improvement of each index exceeds 70%, and the correlation coefficient has increased the most. This is because the EX2 measurement point is located on the left bank of the dam, and the data is  
Figure 10: Measurement point EX2 high and low frequency prediction and correlation coefficient. ((**a**) High frequency prediction; (**b**) Linear regression analysis of LSTM; (**c**) Low frequency prediction; (**d**) Linear regression analysis of RF). The prediction results of each measurement point show that the model proposed in this paper solves the problem of inaccurate prediction caused by the strong volatility of dam deformation data to a certain extent. At the same time, the proposed model is significantly better than other advanced models. The proposal of this model provides theoretical knowledge for the analysis of the deformation prediction of important dam types such as RCC dams, and also provides prior knowledge for the construction of the dam safety monitoring system.