## 4 Analysis and Discussions  
### Mode Decomposition Algorithm-Recurrent Neural Network Combined Prediction Model  
Recurrent neural networks (RNNs), long short-term memory networks (LSTM), and gated recurrent units (GRUs) have excelled in the field of tunnel timing prediction and have become the main models widely used in this field because of the ability of these models to efficiently deal with time series data, capture long-term dependencies, and cope with the complex variations that are commonly seen in tunnel construction. First, the RNN is the most basic time series modeling tool, which enables the network to convey information in the time dimension through the connection of recurrent neurons. The inputs at each moment are not only dependent on the signal at the current moment but also related to the state at the previous moment, which is able to capture short-term dependencies in time series data. However, traditional RNNs are prone to gradient vanishing or gradient explosion when dealing with long time sequences, which makes it difficult for the model to effectively learn and memorize long-term dependencies. In tunnel timing prediction, factors such as construction progress, groundwater level changes, and geological conditions are usually strongly time-dependent, so it is difficult to cope with this complex timing problem by relying only on traditional RNNs. To solve this problem, LSTM was developed. LSTM effectively solves the vanishing gradient problem by introducing forgetting gates, input gates, and output gates, which enables the network to better memorize long-term dependency information and thus more accurately model long-term trends and periodic fluctuations in the process of tunnel construction. By adaptively choosing to retain useful information and discard irrelevant information, LSTM is able to consistently and efficiently convey important information over long time series, which is critical in tunnel timing prediction, as the tunneling process may involve multiyear cycles of geologic changes or construction dynamics. Compared with LSTM, the GRU, as a simplified version of LSTM, is structurally more compact and reduces computational complexity by controlling the flow of information only through update gates and reset gates. Although the GRU is structurally simpler than LSTM, it performs no less well than LSTM in dealing with long-term dependencies and is faster to train, especially when the amount of data is small or the computational resources are limited, and the GRU also has high computational efficiency. In tunnel settlement prediction time series prediction, the GRU is able to provide similar prediction accuracy as LSTM while avoiding the computational burden of LSTM; thus, it has high practical value in practical applications. The common advantage of these three models is that they are able to address the variable time series data in the tunnel construction process. Data in tunnel construction usually contain complex nonlinear relationships and are affected by multiple variables. RNNs, long short-term memory (LSTM) networks, and gated recurrent units (GRUs) are able to capture these complex patterns through their powerful nonlinear modeling capabilities, which in turn provide accurate predictions for decision-making during the construction process [41; 42; 43; 44; 45]. Therefore, in this paper, we use these three representative recurrent neural networks combined with EMD, CEEMDAN, WPT, and VMD to construct combined prediction models, and single-step prediction is performed for the data at points YG1-17. The neural networks all use the Adam optimizer with a learning rate of 0.001, two layers of memories of 80 and 100, a dropout rate of 0.2, a batch size of 64, and an epoch of 100 depending on the convergence of the model.