## 4 Analysis and Discussions
### Mode Decomposition Algorithm-Recurrent Neural Network Combined Prediction Model
Taking monitoring point YG1-17 as an example, the control value for the cumulative settlement at this point is 30 mm and the control value for the settlement rate is 3 mm. According to the control standards of this monitoring point, when the monitoring value reaches above the warning threshold, the relevant personnel should respond quickly and take corresponding safety measures to ensure that the risks during the construction process are effectively controlled to ensure the safety and quality of the project. On the basis of the above conditions, each model predicts the data for early warning judgment and compares the early warning results to obtain the prediction results shown in Table 6.  
Table 6 shows that the length of the yellow warning issued for the medium settlement phase of the raw data is 9 days. During this period, the settlement rate in the first 4 days  
Figure 13: The performance of the decomposition algorithm on different measurement points. reached 85% of the control value, indicating that the settlement rate is more drastic in this phase and that the risk during construction is greater. Over the next 5 days, the rate of subsidence decreased to 70% of the control value, showing a slowdown in the rate of subsidence, and although still on alert, the risk was mitigated. During the last five days of the yellow warning period, the cumulative subsidence values and rates reached 70% of the control values, further indicating that the subsidence process, although slowing down, still needs to be closely monitored. The cumulative settlement values remained at 85% of the control value for 95 days following the full yellow warning cycle, a situation that suggests that although the rate of settlement has slowed in the short term, long-term settlement still accumulates more significantly and exceeds the predetermined control thresholds. Moreover, early warning of the mid-term settlement phase is crucial for risk control during construction, as the volatility of data in this phase directly affects the effectiveness of accurate early warning. The core of accurate early warning lies in the efficient fitting of the fluctuating data, especially the accurate prediction of the settlement rate. The GRU, LSTM, and RNN all achieve relatively poor results in the prediction of medium-term warnings because the prediction curve of a single neural network model is usually smoother and it is easier to maintain convergence, but it is difficult to effectively restore the details of fluctuations in the short-term data, which leads to its lower accuracy in predicting short-term fluctuations. Specifically, a single neural network lacks the ability to restore short-term data fluctuations, thus yielding smaller settling prediction rates and larger prediction errors in the face of complex short-term fluctuations. In contrast, the SSA-VMD-GRU model performs best throughout the early warning stage, better capturing the fluctuating characteristics of the data and providing accurate warnings. However, although the SSA-VMD-GRU model outperforms a single neural network in overall performance, it still suffers from some prediction bias and fails to predict short-term fluctuations completely accurately in the yellow alerts for the first four time periods in which the sedimentation rate reaches 85% of the control value. Therefore, although the SSA-VMD-GRU model has high prediction accuracy, it still has some limitations under certain extreme fluctuations and needs further optimization.