## 3 Case Study
### Comparison of Effects Before and After Improvement
#### 3.5.1 Prediction Accuracy  
The performance of the Informer algorithm under different improvement measures is presented in Figure 9. When standard convolution was replaced with dilated causal convolution, the algorithm showed consistent improvement across all metrics: MSE decreased by 1.3% (5.22\(\rightarrow\)5.15), MAE decreased substantially by 21.2% (5.91\(\rightarrow\)4.66), R\({}^{2}\) increased from 0.79 to 0.80, and RMSE decreased from 2.28 to 2.27. These gains demonstrate the effectiveness of dilated convolutions in capturing long-range temporal dependencies while enforcing causality.  
Subsequent improvements revealed nuanced but meaningful trends: 1 Stratum classification improved overall model efficiency (R\({}^{2}\)\(\uparrow\)0.02 to 0.82, MSE\(\downarrow\)0.4% to 5.13, and RMSE\(\downarrow\)0.4% to 2.26) but increased MAE by 23.6% (4.66\(\rightarrow\)5.76). This indicates that while dimensionality reduction enhanced global pattern recognition (reflected in R\({}^{2}\)/RMSE), it introduced minor localized errors across multiple points without causing significant anomalies. 2 The moving prediction window further enhanced accuracy: MSE decreased  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & **Hyperparameter** & **Optimal Value** \\  & **Optimization Range** & **optimal Value** \\ \hline enc\_layers & [2; 3] & 2 \\ dec\_layers & [1; 3] & 2 \\ n\_heads & [2; 8] & 3 \\ e\_layers & [1; 2] & 2 \\ d\_ff & [128; 512] & 174 \\ factor & [1; 5] & 3 \\ dropout & [0.0, 0.5] & 0.3 \\ embed & [’fixed’, ’learned’] & ’learned’ \\ activation & [’relu’, ’gelu’] & ’gelu’ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal hyperparameter settings for the improved Informer algorithm.  
Figure 9: Changes in evaluation indicators under different improvement measures.  
by 2.7% (5.13\(\rightarrow\)4.99), MAE decreased by 19.1% (5.76\(\rightarrow\)4.66), R\({}^{2}\) increased to 0.83, and RMSE decreased by 1.3% to 2.23. The alignment of all metrics confirms its effectiveness in addressing settlement lag. 3 Special factor encoding delivered the most substantial incremental gain: MSE decreased by 13.6% (4.99\(\rightarrow\)4.31), MAE decreased by 14.6% (4.66\(\rightarrow\)3.98), R\({}^{2}\) increased to 0.84, and RMSE decreased by 6.7% (2.23\(\rightarrow\)2.08). This highlights the critical role of accounting for abrupt construction events. Cumulatively, the fully improved model achieved the following: 20.9% reduction in MSE (5.22\(\rightarrow\)4.13), 38.2% reduction in MAE (5.91\(\rightarrow\)3.65), 11.0% reduction in RMSE (2.28\(\rightarrow\)2.03), and R\({}^{2}\) improvement from 0.79 to 0.86.  
The progressive enhancement across all four metrics--particularly the >10% reduction in RMSE--confirms significant prediction performance gains. While individual step improvements appear modest in some metrics (e.g., MSE changes of 0.02-0.68), their combined effect yields statistically and practically meaningful advancement for engineering applications.  
Figure 10 presents a comparison between the prediction data and the monitoring data. Compared with the original Informer algorithm, the improved Informer algorithm had a prediction trend and peak-valley settlement values that aligned more closely with the monitoring data. Therefore, the improved Informer algorithm boasts higher prediction accuracy than the original one. At ring 250, the predicted settlement values of the two curves diverged significantly. This is because special influencing factors were labeled as features. As a result, the improved Informer algorithm learned that an unexpected stoppage occurred at this location and made more accurate predictions. The improved Informer algorithm has a significantly enhanced capability for predicting surface settlement.