# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.3 Analysis of STAGCN-IEALL
As ST-ARIMA, FC-LSTM, and DCRNN take into account the spatial association of multiple measurement points, the prediction effect of the three models is better than the above three one-sided point models. ST-GCN uses graph convolution model to fully mine the spatial correlation between multiple measurement points and achieves better results than traditional spatio-temporal models. STAGCN-IEALL proposed in this paper uses GCN to extract spatial features and IEALL to extract temporal features, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **MAE** \\ \hline ARIMA-LSTM & 1.1762 & 1.0961 & 6.5841 \\ EALL & 0.9181 & 0.7386 & 4.2325 \\ IEALL & 0.7113 & 0.5127 & 2.4736 \\ ST-ARIMA & 0.4698 & 0.3492 & 1.1421 \\ FC-LSTM & 0.5019 & 0.3973 & 1.3409 \\ DCRNN & 0.4236 & 0.3082 & 0.8521 \\ ST-GCN & 0.3617 & 0.1529 & 0.5327 \\ STAGCN-IEALL & 0.3036 & 0.1304 & 0.4158 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction results of different models. and on this basis a spatio-temporal attention mechanism is introduced to fully consider the dynamic changes between measurement points, so as to ensure that the model can learn the dynamic correlation between different measurement points at any time and the dynamic correlation dependence of any measurement point at different times. Compared with the comparison model, the values of RMSE, MAE, and MAPE are the closest to 0, which are 16.06%, 14.72%, and 21.19% lower than those of ST-GCN, respectively. Therefore, the prediction performance of STAGCN-IEALL model proposed in this paper is better. Experiment 2: Comparative analysis of predictive results under different prediction lengths of each model. In order to more comprehensively verify the prediction performance and stability of the dam deformation spatio-temporal prediction model based on the STAGCN-IEALL, this experiment uses the above eight models to predict the deformation series under different prediction durations, continuously changing the value of the time dimension \(T\) in the model and at the same time calculating the prediction performance under the three indicators of different models, then the results are analyzed. The experimental results are shown in Figure 11.  
It can be seen from Figure 11 that for the three single-point prediction models, the IEALL model has the best prediction effect. Because the single measuring point prediction model does not consider the spatial correlation between measuring points, the prediction performance gradually decreases with the increase of the prediction time. The four tradi  
Figure 11: Variation curves of three evaluation indexes under different duration. (**a**) Curve of RMSE variation in different durations; (**b**) Curve of MAE variation in different durations; (**c**) Curve of MAPE variation in different durations. tional spatio-temporal prediction models, ST-ARIMA, FC-LSTM, DCRNN and ST-GCN, have better prediction performance than the single-point model because of considering the spatial relationship of multiple measurement points. With the increase of prediction time, the prediction performance decreases, but the decline rate is lower than that of the single-point prediction model. It shows that the spatio-temporal prediction model considering spatial factors is more suitable for long-term prediction deformation tasks. STAGCN-IEALL proposed in this paper not only considers the spatio-temporal correlation, but also fully considers the dynamic changes between the measurement points to ensure that the model can learn the dynamic correlation between different measurement points at any time and the dynamic correlation dependence of any measurement point at different times, so as to improve the prediction accuracy of the model. It can be seen from the figure that with the increase of the prediction time, the prediction accuracy of the model is improved. It has always maintained the lowest RMSE, MAE, and MAPE values, and the growth rate is relatively stable. In conclusion, STAGCN-IEALL model is superior to other models.