## 5 Conclusions  
(1) The study employed numerical experimental strategies using variables such as the population size of the SSA, the iteration count, and the proportion of discoverers. A numerical relationship between each indicator and fitness was established, which was subsequently used to determine the optimal values for the model parameters. The results revealed that when the swarm count was 30, the iteration count was 25, the alert value was 0.7, the PD was 0.4, and the SD was 0.2 the SSA-LSTM ground settlement prediction model demonstrated optimal performance. The optimal parameters obtained through numerical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & **MAE** & **MSE** & **RMSE** \\ \hline
1 & 0.0212 & 0.0007 & 0.0269 \\
2 & 0.0229 & 0.0010 & 0.0319 \\
3 & 0.0155 & 0.0004 & 0.0204 \\
4 & 0.0149 & 0.0004 & 0.0210 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation indicators for prediction results of different monitoring points.  
Figure 11: Prediction results at monitoring point 3.  
Figure 12: Prediction results at monitoring point 4.  
experiments can optimize the combined model more effectively, enabling it to be better applied in practical engineering applications.  
(2) The experiments further indicate that combining multiple models often achieves better performance than any single model. For instance, ensemble models can effectively reduce the variance in the model, thereby decreasing the risk of overfitting. Ensemble models can combine the advantages of various models, performing better than a singular model. A single model might overly focus on a specific aspect of the data and neglect others, while ensemble models can achieve a better balance. Different models might make distinct mistakes. When combined, their errors tend to cancel each other out, resulting in a lower overall error rate. The outputs of ensemble models are generally more stable.  
(3) From the evaluation of the predicted values at the monitoring points and the depiction of the prediction outcomes, it was clear that the SSA-LSTM model significantly improved prediction accuracy. Furthermore, the adaptive features of the SSA-LSTM model were fully utilized, demonstrating its strong generalization capability. This provided a novel method for predicting ground settlement in areas affected by multiple factors. In addition, compared with other combined models, this model simplified the operational process and enhanced its practicality in ground settlement prediction. However, when applying the SSA-LSTM model, the segmentation strategy for input and output data still needs further exploration and optimization.  
Conceptualization, P.Q., F.L. and J.Z.; methodology, J.Z.; software, F.L.; validation, P.Q. and F.L.; formal analysis, P.Q.; investigation, P.Q. and J.Z.; resources, F.L.; writing--original draft preparation, P.Q. and J.Z.; writing--review and editing, P.Q. and J.Z.; visualization, F.L.; supervision, F.L.; project administration, F.L.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China, grant number 41807258.  
Not applicable.  
All data included in this study are available upon request to the corresponding author.  
The authors declare no conflict of interest.