# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.2 Analysis of IEALL
As a consequence, the comprehensive optimization performance was higher, which serves as a useful foundation for the second experiment. Experiment 2: Test and verification of IEALL optimization performance. The IGWO algorithm is used to optimize the LSTM model parameters in the EALL. Then, in order to verify whether the optimized parameters are optimal, the IEALL model is constructed, constantly changing the current parameters and bringing them into the model for prediction while keeping other parameters constant, recording the RMSE values of the model when each parameter takes different values. The parameters to be optimized are \(epoch\), \(batch\_size\), \(num\), and \(\eta\), etc. In order to verify the impact of the optimized parameters on the model accuracy, the above parameters are experimented and analyzed one by one. Firstly, \(epoch\), \(batch\_size\), \(num\), and \(\eta\) are initialized, then IGWO is used to optimize each parameter. The specific experimental procedure is as follows. (1) The \(epoch\), \(batch\_size\), \(num\), and \(\eta\) are optimized by the IGWO algorithm. Where \(epoch\) is set to 10 and the range is set to [10, 200], \(batch\_size\) is set to 1 and the range is set to [1, 100], \(num\) is set to 2 and the range is set to [2, 50], \(\eta\) is 0.001 and the range is set to [0.001-0.01], IGWO is used to optimize the current parameters within the parameter range while keeping the other three parameters unchanged. The experimental results show that \(epoch=153\), \(batch\_size=36\), \(num=13\), and \(\eta=0.0077\), which are the selected parameters of EALL in this section. (2) To verify the effectiveness of the parameters after the IGWO optimization, the trend of the RMSE values of the predicted results of the EALL under the change of the current parameters is calculated by keeping the other three parameters unchanged. The experimental results are shown in Figure 9.  
As can be seen from Figure 9a, when other parameters remain unchanged, the RMSE value of EALL model first decreases with the increase of training times. When \(epoch=153\), the model RMSE value reaches the minimum value. However, with the continuous increase of training times, the model slowly enters the state of overfitting, and the RMSE value gradually increases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Benchmark Function** & **PSO** & **GWO** & **IGWO** \\ \hline Sphere & 0.4339 & \(8.13\times 10^{-7}\) & 0 \\ [PERSON] & 3.8301 & \(2.19\times 10^{-4}\) & \(2.92\times 10^{-5}\) \\ [PERSON] & 15.1984 & 0.8153 & \(9.21\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean square error test results of different models. Batch size refers to the number of samples fed into the network training at each time. When it is too small, the convergence speed is slow and less stable, and it is easy to fall into the partial optimum, while if the batch size is too large, it is easy to cause problems such as missing the nadir during gradient descent and thus reducing the accuracy of the model. As can be seen from Figure 9b, the minimum value is obtained when \(batch\_size=36\). The number of hidden layer units also has a certain influence on the prediction results, it can be seen from Figure 9c that as the number of hidden layers increases, the internal structure of the model becomes increasingly complex, which is prone to problems such as overfitting and long training time, then leading to gradual increase in the RMSE of the model; the RMSE value of the model is the smallest when \(num=13\). Learning rate determines whether the target can converge to the global minimum and the convergence speed. When the learning rate is too small, the convergence process is slow, while if it is too high will lead to overfitting or failure to converge. As can be seen from Figure 9d, the overall effect of the learning rate on the prediction accuracy decreases first and then improves, and the RMSE reaches the minimum value at \(\eta=0.0077\).