###### Abstract  
The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial-temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. First, the original data are optimized via the SSA, which has global optimization capability, high noise immunity, and high adaptivity. The original signal is subsequently decomposed into multiple subsial sequences via a variational modal decomposition (VMD) algorithm combined with a rolling decomposition technique. Finally, the decomposed signals are fed into the machine learning model to construct a high-precision settlement prediction model based on rolling decomposition. The prediction accuracy of different models was analyzed via the measured settlement data during the construction period of the Beijing subway as an example. The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model. construction safety; prediction accuracy; adaptive variational mode decomposition; bench method construction; time series prediction; data preprocessing 202  
Traditional subway tunnel deformation research is based mainly on theoretical calculations, field data analysis, model tests, and finite element numerical simulations, and many fruitful results have been obtained [3; 4; 5; 6; 7; 8]. However, with the increasing complexity of underground engineering conditions, the above methods have certain limitations in their application, such as the reliance on idealized assumptions, the complexity of model parameter settings, and the difficulty of the test process in fully reproducing the actual working conditions. These limitations often lead to large deviations between deformation prediction results and measured data. For example, it is difficult to obtain accurate structural and mechanical properties of strata, and it does not involve real-time dynamic prediction [9]. Another method involves analyzing and deciphering the inner relationships and development laws of a large number of historical monitoring data through mathematical and statistical analyses and then forecasting the settlement value through methods such as the autoregressive model and random medium theory [10]. The data required for this method are relatively easy to obtain, but the empirical method is unable to quantify accurate settlement results because of geological conditions and construction methods [11]. To compensate for the shortcomings of traditional calculation methods, neural network analysis has been gradually introduced into deformation studies of tunnel engineering and underground engineering. Owing to their powerful nonlinear mapping capability and good fault tolerance, neural networks have excellent applicability and advantages in solving deformation prediction problems with high uncertainty, nonlinearity, and significant spatiotemporal variation characteristics [12]. Neural networks such as backpropagation, generalized regression, and long short-term memory (LSTM) are widely used for surface subsidence prediction because of their good data depth representation [13; 14; 15; 16; 17]. [PERSON] and [PERSON] used three methods, namely artificial neural networks, support vector machines, and Gaussian processes (GPs) [18], to predict tunnel excavation surface settlement and reported that the neural network prediction method succeeded in reducing the error rate to 13%, 12.8%, and 9%, respectively.