## 6 Discussion  
In this section, the influence of different statistical population size on IJaya algorithm and prediction performance of different models are discussed to verify the superior prediction ability of the proposed model. (1) Comparison of different statistical population size  
Suitable statistical population size is beneficial to enhance the optimization ability of IJaya algorithm. In order to find the optimal statistical population size of IJaya algorithm in terms of grouting power prediction, different sizes which are 10, 20, 30, 40 are chosen. The comparison results are depicted in Figure 8.  
From Figure 8, a conclusion can be drawn that the optimization ability of IJaya algorithm is best when the statistical population size is 30. When decreasing the statistical population size of IJaya algorithm from 30, the performance of this algorithm gets worse. What's more, when increasing the statistical population size beyond 30, the performance also gets worse. Therefore, the statistical population size is chosen as 30 in this paper. (2) Prediction performance of different models  
It can be seen from above that the performance of EPIJaya-SVR in grouting power prediction is good, but it still needs to be compared with the other models (EIJaya-SVR, IJaya-SVR, Jaya-SVR, and SVR) considered here. EIJaya-SVR uses the historical values from lag 1 to lag 20 to construct input vectors for each subseries, and the other models all use the PACF to construct the optimal input vectors. The final prediction results of these five prediction models are depicted in Figure 9, and the results of the four performance indices and the DM test are listed in Table 2 and illustrated in Figure 10.  
Figure 8: Selection of statistical population size of IJaya algorithm. Figure 10: The results of performance indices and DM test of different models. Figure 9: Actual grouting power and the prediction results of different models. From Table 2 and Figure 10, through the comparison of all the models, it is obvious that the EPIJaya-SVR model shows the best performance by obtaining the best RMSE, MAE, MAPE, and EC values of 0.2672 MPa-L/min, 0.2165 MPa-L/min, 3.85%, and 0.9815, respectively. Though the prediction performance of the EIJaya-SVR model with RMSE, MAE, MAPE, and EC values of 0.2788 MPa-L/min, 0.2252 MPa-L/min, 4.00%, and 0.9806, respectively, is little worse than that of the EPIJaya-SVR model, it still performs better than any other single model. This means that the EWT plays a significant role in improving prediction accuracy by denoising and transforming the nonlinear grouting power series into several relatively stable and regular subseries. In general, the performance of forecasting models is supposed to be enhanced by a carefully selected set of input variables because the nonlinear features of grouting power can be effectively captured. However, the input variables for predictors are determined artificially, which leads to poor prediction accuracy. In this study, the PACF was employed to select variables objectively, thereby effectively avoiding the subjectivity and randomness caused by manual selection. As given in Table 2 and shown in Figure 10, the proposed hybrid model betters EIJaya-SVR with reductions of about 4.16%, 3.86%, and 3.75% in the values of RMSE, MAE, and MAPE and a 0.09% rise in the EC value. Hence, utilizing the PACF to select variables reasonably as the input vectors can improve prediction accuracy. It also can be clearly observed from Table 2 and Figure 10 that IJaya-SVR and Jaya-SVR models outperform the SVR model with lower values of RMSE, MAE, MAPE and higher values of EC. This demonstrates the prediction ability of SVR model can be improved by adopting optimization algorithm to optimize hyperparameters. Besides, IJaya-SVR offers significant prediction superiority over Jaya-SVR with lower values of RMSE (1.1262 MPa-L/min), MAE (0.5456 MPa-L/min), and MAPE (8.96%) and a higher EC (0.9527).