###### Abstract
Although the prediction accuracy of neural networks has been greatly improved compared with that of traditional prediction methods, a single machine learning algorithm usually has difficulty quickly identifying effective rules from the data and building a model with practical and accurate prediction capability when faced with time series data such as tunnel excavation surface settlement data, which include extremely complex physical processes, strong nonlinearities, noisy disturbances, and short-term volatility. To further enhance its ability to extract time series information as well as anti-interference generalization, a combination of machine learning decomposition algorithms is often used [19; 20; 21]. [PERSON] created a WPT-LSSVM prediction model, used LSSVMs to predict the target sequences at high and low frequencies, and reconstructed the predicted sequences to output the predicted values; the prediction results revealed that the mean absolute error and root mean square error of the WPT-LSSVM model were significantly smaller than those of the traditional LSSVM models [22]. To address the non-stationarity and nonlinearity of structural deformation data, [PERSON] considered the influence of different data components within the monitoring data on the accuracy of the prediction model and proposed a combined prediction model based on EMD, support vector regression, and a wavelet neural network (WNN). The results showed that the combined prediction model significantly improved the prediction accuracy in comparison with the support vector regression and WNN models [23]. In addition, VMD has been extensively researched, owing to its comprehensive mathematical and theoretical foundation, which ensures better robustness and effective separation of the original signal [24]. It is widely used in various areas, such as mechanical fault detection [25], image signal analysis [26], and wind speed prediction [27; 28], where it has exhibited good performance in handling data with strong nonlinearity. Discussions on the accuracy of neural networks for sediment prediction have always faced disagreement, and with the development of large machine learning and deep learning models, an increasing number of studies have shown that it is beneficial to extract effective information by substituting decomposition algorithms, which in turn improves the prediction accuracy of sedimentation models. For example, the variational modal decomposition (VMD) algorithm can effectively prevent the occurrence of modal mixing phenomena, and the empirical modal decomposition (EMD) algorithm can be used for adaptive data [29; 30; 31]. However, these studies failed to discuss in depth the selection criteria for decomposition algorithms or evaluate the performance of different decomposition algorithms, making it difficult to accurately select the best combination of predictive models. Moreover, the problem of information leakage leads to an inflated accuracy of the model, which cannot be effectively used in practical engineering. Therefore, to ensure accurate predictions on the basis of measured data and the timely adjustment of support parameters and reserved deformation to ensure construction safety, appropriate decomposition algorithms must be selected. In summary, to further improve the accuracy of the settlement prediction model during subway tunnel construction, this paper proposes a tunnel deformation prediction method that combines data decomposition, reconstruction, and optimization, aiming at overcoming the lack of accuracy and complexity of the traditional method, especially when facing the complex settlement prediction task during the subway construction period. The method first preprocesses and optimizes the raw monitoring data via the SSA with global optimization capability, strong noise immunity, and high adaptivity.