## 3 Results
### Analysis of Deformation Predictions  
In view of the characteristics of the dataset studied in this paper, which is a small sample data, four machine learning models applicable to the prediction of small sample data are selected, namely decision tree regression model (DTR), support vector regression model (SVR), Gaussian process regression model (GPR), and stacked machine learning method (stacked ML) [40]. The prediction model is trained by rolling time series to find the optimal prediction model under the best time series. Considering the close proximity of Songxi Station and Luochongwei Station of Guangzhou Metro Line 13, the geological and hydrological conditions are similar. Therefore, the deformation data of Songxi station for a complete monitoring cycle are collected for model training, and then the trained model is used for deformation prediction at Luochongwei station. The data of 20 monitoring points in Songxi station are taken as the data set, 14 of which are selected as the training set, and the remaining 6 are the test set. The generalization ability of the model is evaluated by the average error of the test set. Firstly, monitoring data are collected from the Songxi station. Figure 4 displays the data of a monitoring point at Songxi station. Surface settlement, groundwater levels, and horizontal pile displacements tend to increase over time and fluctuate within a small range. And the corresponding rates of change shows an overall decreasing trend over time and fluctuate within a small range. The cumulative change and the rate of change have a similar pattern, both of which show a rise or fall before reaching a steady state. The prediction model has a uniformity, and the cumulative change is used to illustrate the model optimization process. Secondly, a five-fold cross-validation grid search method is used to find the optimal parameters of the model. As shown in Figure 5, the optimal parameters for DTR are {max_depth, min_samples_leaf} = [20, 2], and the corresponding error value is 0.1. The optimal parameters SVM are [error term penalty parameter \(C\), kernel function coefficient \(gamma\)] = (1, 0.25), and the corresponding error value is 0.12. The important parameter of GPR is the kernel function, the radial basis kernel function is chosen in this paper, and the corresponding error value is 0.22. The optimal parameters of the stacked ML are the data from reference. Finally, a rolling time series approach is used to construct the training model. If the input sequence is too short or the output sequence is too long, it is easy to produce under-fitting, and vice versa. Therefore, selecting the appropriate input and output sequence length directly affects the model effect. For subway foundation \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Indictors**} & \multicolumn{4}{c}{**Early-Warning Level**} \\ \hline
**Primary Indicators** & **Secondary Indicators** & **Safe** & **Yellow** & **Orange** & **Alam** \\  & & & **Early-Warning** & **Early-Warning** & **Alarm** \\ \hline surface settlement (E1) & cumulative value k1 & & & case1: \\  & rate of change k2 & case1: & 0.7 \(<\) k\({}_{1}\)\(<\) 0.85, & 0.85 \(<\) k\({}_{1}\)\(<\) 1, & case1: \\ groundwater level (E2) & cumulative value k1 & 0 \(<\) k\({}_{1}\)\(<\) 0.7, & 0.7 \(<\) k\({}_{2}\)\(<\) 0.85 & 0.85 \(<\) k\({}_{2}\)\(<\) 1 & 1 \(<\) k\({}_{1}\)\(<\) 1.6, \\  & rate of change k2 & 0 \(<\) k\({}_{2}\)\(<\) 0.7 & case2: 0.85 \(<\) k\({}_{1}\)\(<\) 1 & case2: 1 \(<\) k\({}_{1}\)\(<\) 1.6 & 1 \(<\) k\({}_{2}\)\(<\) 1.6 \\  & cumulative value k1 & & case3: 0.85 \(<\) k\({}_{2}\)\(<\) 1 & case3: 1 \(<\) k\({}_{2}\)\(<\) 1.6 & \\  & rate of change k2 & & & case3: 1 \(<\) k\({}_{2}\)\(<\) 1.6 & \\ \hline \end{tabular}
\end{table} 
Table 4: Early-warning classification criteria. pit engineering, by predicting the data of the next 3 to 7 days, decision makers can have enough time to respond to emergencies.