## 4 Result
### Results of GWO-LSTM Prediction Model  
In this study, the combined method of the Grey Wolf Optimization algorithm (GWO) and Long Short-Term Memory network (LSTM) was used to predict the time series of land surface subsidence in different subsidence areas of Kunming City. The results of LSTM parameter optimization and the prediction accuracy index are shown in Table 4.  
In subsidence zone A, the GWO-LSTM model showed high prediction accuracy, with an RMSE and MAE of 3.5438 and 2.8288, and a coefficient of determination (R\({}^{2}\)) of 0.93532. The model can capture the dynamic change in land surface settlement in this region with high accuracy. The prediction results of subsidence zone B show that the model also has a good performance, with an RMSE of 2.7745, MAE of 1.8907, and R\({}^{2}\) of 0.93063. These results show that the GWO-LSTM model is reliable in predicting the surface change in subsidence zone B. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subsidence Area** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Subsidence zone A & 3.5438 & 2.8288 & 0.93532 \\ Subsidence zone B & 2.7745 & 1.8907 & 0.93063 \\ Subsidence zone C & 2.9179 & 2.1371 & 0.93966 \\ Subsidence zone D & 2.9671 & 2.1651 & 0.93713 \\ Subsidence zone E & 2.6812 & 1.7788 & 0.8379 \\ Subsidence zone F & 3.4631 & 1.6571 & 0.94862 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction accuracy index. Figure 14: Time series monitoring results of Kunming urban area. In subsidence zone C, the model also showed robust forecasting ability, with an RMSE and MAE of 2.9179 and 2.1371, and an R\({}^{2}\) of 0.93966, which was the second-highest R\({}^{2}\) value in all regions. The prediction of subsidence zone D shows that the performance of the model is also well maintained here, with an RMSE of 2.9671, MAE of 2.1651, and R\({}^{2}\) of 0.93713. It is worth noting that the prediction performance of subsidence zone E is relatively low: its RMSE and MAE values are 2.6812 and 1.7788. The coefficient of determination R\({}^{2}\) is 0.8379, which is lower than that of other regions. This indicates that the settlement dynamics in this region may have been affected by more complex factors, and further analysis could improve the prediction accuracy. Finally, in subsidence zone F, the GWO-LSTM model shows the best prediction performance. With RMSE and MAE values of 3.4631 and 1.6571, and an R\({}^{2}\) up to 0.94862, the prediction of the model in this region is highly consistent with the actual observed data. In general, through the construction of GWO-LSTM, the proposed model has demonstrated good forecasting ability in multiple settling areas of Kunming City. This provides reliable support for the monitoring and prevention of land surface settlement in this area in the future. Nevertheless, the relatively low prediction accuracy of subsidence zone E suggests that we need to pay attention to further optimization of the model in future work. This identifies potential factors that may affect the prediction performance of this region. The LSTM neural network optimized based on the Grey Wolf Optimizer algorithm had a time step of 5, and used the data of five periods as historical data for point-to-point prediction. The number of grey wolves was set to 3 to optimize the three parameters of LSTM. The prediction process and results are shown in Figure 15. The error values of each monitoring point in each area converge around 0, and there is no concentrated monitoring point error showing an abrupt scattering transition. The prediction results and the test set show a high coincidence phenomenon on the graph. The prediction results have an excellent ability to show the characteristics of historical data changes. The historical data and monitoring results were fitted according to the beginning, middle, and forecast results (Figure 16). The upper two layers were used as the historical monitoring data, and the bottom figure was used as the future forecast data.