## 2 Principle and Construction of the Settlement Prediction Model
### Improved VMD Based on the SSA  
The decomposition accuracy of VMD mainly depends on the decomposition number \(K\) and penalty factor \(a\). If \(K\) is too small, adequate information cannot be extracted; conversely, if the value of \(K\) is too large, excessive decomposition will occur and some false signals will be generated. The bandwidth of each modal component is influenced by the penalty factor \(a\); a value of \(a\) that is too large or too small will lower the extraction effect of the signal. Owing to the complexity and variability of surface sedimentation sequence signals, setting these parameters artificially is difficult, which increases the likelihood of unsatisfactory decomposition results. Therefore, this paper proposes optimizing the parameters of VMD via the SSA. The combination of the adaptive VMD improvement algorithm with the neural network forecasting method is shown in Figure 2.  
On the basis of the flow in Figure 2, we can generalize the corresponding construction steps. The whole process includes data acquisition, preprocessing, model optimization,  
Figure 2: The framework of the combination of adaptive decomposition and the surface subsidence prediction model. and prediction. Time series signals of surface settlement and other settlement-related parameters are collected through real-time monitoring or historical records. The acquired signals usually contain noise and outliers, so data preprocessing, including denoising, normalization, and outlier rejection, is required to ensure data quality, which in turn provides an accurate basis for subsequent modeling. After the data preprocessing is complete, the prediction model is optimized via the SSA. First, the initial conditions of the SSA are set, including the number of iterations, population size, and convergence criteria, to ensure that the algorithm can search effectively for the global optimal solution. In this process, the goal of optimization is to determine the optimal number of layers and penalty factor for the VMD algorithm. The selection of these optimization parameters directly affects the accuracy of the signal decomposition and the predictive ability of the model. Therefore, it is necessary to set a reasonable optimization space and select the VMD parameter with the smallest entropy value as the optimal configuration through statistical calculations to minimize the MEP (minimum entropy penalty) criterion. Next, the original settling signal is decomposed into multiple intrinsic modal functions via the VMD algorithm combined with the roll decomposition technique, with each IMF representing a different frequency component of the signal. The roll decomposition technique enhances the model's ability to adapt to temporal variations and helps capture the complex temporal and spatial variability characteristics in settlement data. Through this process, the settling signal is effectively decomposed into a series of relatively simple signal sequences, providing a clearer signal structure for subsequent predictive analysis. Through this process, the settling signal is effectively decomposed into a series of relatively simple signal sequences, providing a clearer signal structure for subsequent predictive analysis. Finally, the individual signaling components obtained from the decomposition are input into a neural network prediction model, which is trained and learns the relationships between different components and settlement behavior on the basis of historical data. The prediction results of the neural network are combined with the predicted values of each signaling component and superimposed to obtain the final settlement prediction. Traditional decomposition integration models directly decompose the entire original sequence into distinct subsequences one at a time. Unknown future values can unintentionally affect the decomposition of specific data points, leading to information leakage.