## 4 Results and Discussion
### Comparative Analysis of Different Models
#### 4.2.2 Comparison of Prediction Errors at Different Cooling Stages  
Error analysis was conducted by dividing the 0-21-day period into two phases based on the intensity of the cement hydration reaction: the temperature rise period (0-8 days), characterized by an enhancement in hydration, and the temperature decline period (8-21 days), marked by a reduction in hydration activity. BP and LSTM are offline-trained models, whereas ISVR and SSA-ISVR are online-trained models. Figures 13-15 show the prediction results for typical pouring blocks across three dam segments. Figure 13: Prediction results of models for the monitoring area in 7#-030: (**a**) temperature of the test set at 0–8 days; (**b**) the regression results of the SSA-ISVR model at 0–8 days; (**c**) temperature of the test set at 8–21 days; (**d**) the regression results of the SSA-ISVR model at 8–21 days. Figure 15: Prediction results of models for the monitoring area in 17#-079: (**a**) temperature of the test set at 0–8 days; (**b**) the regression results of the SSA-ISVR model at 0–8 days; (**c**) temperature of the test set at 8–21 days; (**d**) the regression results of the SSA-ISVR model at 8–21 days. \begin{table}
\begin{tabular}{c c c c c} \hline
**Test Sample** & **Algorithm** & **RMSE** & **Test** & \\
**Set** & **BP** & 0.589 & 0.328 & 0.779 \\  & LSTM & 0.520 & 0.352 & 0.917 \\  & ISVR & 0.507 & 0.326 & 0.928 \\  & SSA-ISVR & 0.339 & 0.241 & 0.960 \\ \hline  & BP & 0.410 & 0.188 & 0.870 \\
8–21 days & LSTM & 0.372 & 0.233 & 0.915 \\  & ISVR & 0.381 & 0.215 & 0.920 \\  & SSA-ISVR & 0.248 & 0.173 & 0.958 \\ \hline \end{tabular}
\end{table} 
Table 7: Evaluation of different model test samples in 7#-030. According to results in Tables 6-9, the pretrained model in this study achieved an \(RMSE\) of 0.19 and an \(MAE\) of 0.08 \({}^{\circ}\)C, making it slightly less effective than the BP model. The higher performance index on the training set comes from the fact that the training samples adequately reflect the overall distribution of the data. There is a significant statistical relationship between the variables, indicating that the model used has good fitting ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Sample** & **Algorithm** & & **Test** & \\
**Set** & **Algorithm** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{0–8 days} & BP & 0.810 & 0.538 & 0.910 \\  & LSTM & 0.716 & 0.654 & 0.949 \\  & ISVR & 0.748 & 0.539 & 0.933 \\  & SSA-ISVR & 0.486 & 0.353 & 0.981 \\ \hline \multirow{4}{*}{8–21 days} & BP & 0.564 & 0.306 & 0.914 \\  & LSTM & 0.596 & 0.541 & 0.932 \\ \cline{1-1}  & ISVR & 0.538 & 0.328 & 0.934 \\ \cline{1-1}  & SSA-ISVR & 0.361 & 0.263 & 0.980 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation of different model test samples in 11#-012. Figure 16: Model evaluation in 7#-030: (**a**) RMSE and MAE of different models in two tests and (**b**) R\({}^{2}\) of different models in two tests. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Sample** & **Algorithm** & & **Test** & \\
**Set** & **Algorithm** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{0–8 days} & BP & 0.667 & 0.440 & 0.873 \\  & LSTM & 0.751 & 0.640 & 0.895 \\  & ISVR & 0.565 & 0.431 & 0.941 \\  & SSA-ISVR & 0.435 & 0.298 & 0.965 \\ \hline \multirow{4}{*}{8–21 days} & BP & 0.466 & 0.255 & 0.892 \\  & LSTM & 0.557 & 0.436 & 0.885 \\ \cline{1-1}  & ISVR & 0.414 & 0.276 & 0.941 \\ \cline{1-1}  & SSA-ISVR & 0.307 & 0.179 & 0.966 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation of different model test samples in 17#-079. However, there are differences in the generalization performance of different models. In the 0-8-day and 8-21-day stages, the SSA-ISVR model achieved an average \(RMSE\) of 0.20 and an average \(MAE\) of 0.14 \({}^{\circ}\)C for temperature predictions across multiple blocks, yielding the best prediction accuracy across all tested sections. As demonstrated in Figures 13-15, the online models generally exhibited smaller prediction errors.