# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 3 Results and Discussion  
In this section, the sub-sequences of different frequencies obtained after the decomposition of the two measuring points are input into LSTM and RF, respectively, and obtain the corresponding predicted values. To evaluate the applicability of the model, the prediction effects of LSTM and RF on high and low frequency data are discussed through two monitoring points EX5 and EX2, analyzing and explaining them based on the correlation coefficient. Finally, comparing the proposed model with the traditional prediction model, it is found that the model has excellent practicability for dam deformation prediction. As shown in Figures 9a and 10a, LSTM has great advantages in capturing the inflection point of high-frequency data, but occasionally there may be situations where it cannot be captured. After our analysis, the reason may be the data's volatility is too strong. Figures 9b and 10b show that the correlation between the predicted value and the true value at the two measuring points reached 95.3% and 95.0%, respectively. As can be seen, most of the prediction points are within the 95% confidence interval, which shows that LSTM has a strong ability to mine high-frequency data. Figures 9c and 10c indicate that RF also has a good effect in realizing the prediction of stable data, which is not only reflected in the trend of the data, but also in the time lag. It can be seen from Figures 9d and 10d, although the residual sequence is an uncertain component, the predicted correlation coefficient values at the two measurement points also reached 99.4% and 98.1%, respectively, which proved that the input of low frequency components into RF is effective. Therefore, it is considered that RF has a positive significance for the prediction of low-frequency components. Then, the high-frequency, low-frequency, and residual prediction results are reconstructed, and finally the final prediction value of dam deformation is obtained. This section selects a single LSTM, RF and Extreme Learning Machine (ELM) as the comparison model. Compared with the traditional single hidden layer neural network, ELM has the advantages of fewer parameters, fast training speed and strong generalization ability [16]. The comparison result is shown in Figure 11. From Figure 11a, it can be seen that the predicted trends of each model are roughly the same as the actual measured trends, but the predicted results of the proposed model are closer to the real results, and at the same time, it can better capture the mutation points of the dam deformation. From Figure 11b, it can be found that the absolute error of the model is the smallest, which illustrates the accuracy of the model's prediction and its reliability in the prediction of concrete dam deformation. In order to detect the outliers between the observed value and the predicted value, this study uses box plots to further illustrate. The box plots do not rely on any assumptions and are very robust to outliers. Figure 11c shows the box line drawing results of the proposed model and other comparison models on measuring point EX5. It can be found that the residuals of the model are distributed within 1.5 quartiles (IQR), and there are almost no extreme \begin{table}
\begin{tabular}{c c c c} \hline  & **Bootstrap** & **Max\_Depth** & **n\_Estimators** \\ \hline Domain & [True, False] & [1, 20] & [2, 30] \\ EX5 & True & 10 & 18 \\ \hline \end{tabular}
\end{table} 
Table 4: RF parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Point** & **Hidden Layer** & **Units and Dropout** & **Parameters** & **Setting** \\ \hline \multirow{3}{*}{EX5} & LSTM Layer1 & Units = 65, Dropout = 0.2 & Batch\_size & 48 \\  & LSTM Layer2 & Units = 90, Dropout = 0.25 & Optimizer & Adam \\  & LSTM Layer3 & Units = 80, Dropout = 0.4 & Activation Function & Relu \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LSTM parameters. outliers, with only a few mild outliers.