# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.3 Analysis of STAGCN-IEALL  
(1) Parameter setting  
The basic parameters of the experiment are time dimension \(T\) is 2409, the number of nodes \(N\) is 5, the dimension of measurement point features \(D\) is 5, and the prediction time \(K\) is 230. The hyperparameters are set mainly by grid search to determine the optimal parameters, keeping the other hyperparameters constant when determining a parameter. The \(batch\_size\) is taken from the set [8, 16, 32, 64, 128, 256], \(\eta\) is taken from the set [0.0001, 0.0005, 0.001, 0.005, 0.01], the optimizer is selected from [Adam, SGD, Adagrad]. Finally, the results are obtained through multiple experiments on the verification set. The selected \(batch\_size\) is 128, \(\eta\) is 0.001, the optimizer selects Adam. At the same time, all results are taken as the average of multiple experimental results to reduce the experimental error. (2) Baseline methods  
In order to more comprehensively verify the advantages of the method proposed in this section in predicting the dam deformation sequence, the following prediction methods are used for comparative experiments, as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **MAE** \\ \hline EALL & 0.9181 & 4.2325 & 0.7386 \\ IEALL & 0.7113 & 2.4736 & 0.9421 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The evaluation results of IEALL model and EALL models. Figure 10: Comparison of prediction results between IEALL and EALL. * ARIMA-LSTM: The dam deformation prediction model combining autoregressive moving average model and long short-term memory network has stronger prediction ability than a single model. * EALL: The algorithm proposed in Section 4, based on the common dam deformation combination model, decomposes the deformation sequence through EEMD, and gives full play to the respective prediction advantages of ARIMA and LSTM models. Finally, the relationship between the two predicted values is constructed through LSTM model, and the final predicted value is obtained. * IEALL: Based on EALL, IGWO is proposed to optimize two LSTM networks in EALL, so that the LSTM networks can reach the best training parameters, thus improving the prediction accuracy. * ST-ARIMA [21]: Temporal-spatial autoregressive moving average model. By constructing the spatial weight matrix and constructing the spatio-temporal stationary sequence by using the methods of adjacency weighting and inverse distance weighting, good prediction results have been achieved in the multi-station prediction. * FC-LSTM [30]: A prediction model with fully connected LSTM layers is used in both encoder and decoder. * DCRNN [31]: Diffusion convolutional recurrent neural network, which uses random walk on graph to model the prediction task, has achieved good prediction results. * ST-GCN [32]: Spatio-temporal graph convolutional neural networks, which combine graph convolutional layers and convolutional sequences to make predictions. Among them, the first three methods are single measuring point prediction methods only considering time series, and the last four methods are multi-measuring point prediction methods considering spatial correlation. (3) Analysis of experimental results  
Experiment 1: Prediction results and analysis of each model. The deformation series are predicted by the above seven models as well as the STAGCN-IEALL model proposed in this paper, where the single measurement point prediction model uses the single measurement point data and the multi measurement point prediction model uses the deformation data combining the spatial distribution of multiple measurement points to predict the value of a measurement point for 230 days. Through a large number of experiments, the average results of 10 experiments are taken to minimize the error caused by randomness, and the final results are shown in Table 7.  
It can be seen from Table 7 that the prediction results of single measurement point prediction models such as ARIMA-LSTM, EALL, and IEALL are poor, because these three models ignore spatial features.