# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 2 Methodology
### LSPP Model Design
#### 2.2.2 Data Preparation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Models** & **Tunnelling** & **Geological** & **Prediction** \\  & & **Parameters** & **Conditions** & **Target** \\ \hline
[3] & LSTM & \(CP\), \(CT\), \(TF\) & - & \(PR\) \\ \hline
[9] & XGBoost & \(CS\), \(CT\), \(TF\) & \(BTS\), \(UCS\) & \(PR\) \\ \hline
[12] & BPNN & \
\begin{tabular}{c} \(CS\), \(CT\), \(EP\), \\ \(SS\), \(TF\) \\ \end{tabular} &
\begin{tabular}{c} \(CD\), \(ET\), \(K\), \\ \(P\), \(UCS\) \\ \end{tabular} & \(AS\) \\ \hline
[15] & LSTM &
\begin{tabular}{c} \(CS\), \(CT\), \\ \(EP\), \(TF\) \\ \end{tabular} & - & \(PR\) \\ \hline
[19] & \
\begin{tabular}{c} KF-CNN- \\ BiGRU \\ \end{tabular} &
\begin{tabular}{c} \(AD\), \(CT\), \(DR\), \\ \(EP\), \(FR\), \(GP\), \\ \(PA\), \(RA\), \(TF\) \\ \end{tabular} & - & \(AS\) \\ \hline
[20] & LSTM &
\begin{tabular}{c} \(CP\), \(CS\), \(CT\), \\ \(SE\), \(TF\), \(TAP\) \\ \end{tabular} & - & \(PR\) \\ \hline
[21] & \
\begin{tabular}{c} Attention- \\ ResNet- \\ LSTM \\ \end{tabular} &
\begin{tabular}{c} \(CS\), \(CT\), \(TF\) \\ \end{tabular} & \(BC\), \(E\) & \(AS\) \\ \hline
[31] & GRU & \
\begin{tabular}{c} \(CS\), \(EP\), \(FV\), \\ \(FP\), \(GV\), \(GP\), \\ \(PR\), \(TF\) \\ \end{tabular} &
\begin{tabular}{c} \(CD\), \(TMR\), \(TS\), \\ \(TSC\), \(TSWR\) \\ \end{tabular} & \(AS\) \\ \hline
[32] & ANN &
\begin{tabular}{c} \(AD\), \(AVE\), \(CS\), \\ \(CT\), \(EP\), \(GV\), \\ \(GP\), \(SS\), \(TF\), \(JS\) \\ \end{tabular} & - & \(AS\) \\ \hline
[33] & \
\begin{tabular}{c} KCNN- \\ LSTM \\ \end{tabular} &
\begin{tabular}{c} \(AS\), \(CS\), \(CT\), \\ \(PR\), \(TF\) \\ \end{tabular} & \(CD\), \(E\), \(W\), \(\varphi\) & \(CP\) \\ \hline
[34] & GWO-GRNN & \
\begin{tabular}{c} \(CS\), \(CT\), \(EP\), \\ \(FP\), \(GP\), \(GV\), \\ \(PR\), \(TF\) \\ \end{tabular} &
\begin{tabular}{c} \(AS\) \\ \(GS\) \\ \end{tabular} \\ \hline
[35] & \
\begin{tabular}{c} MRFO- \\ AT-TELM \\ \end{tabular} &
\begin{tabular}{c} \(CS\), \(CP\), \(EP\), \\ \(TAP\) \\ \end{tabular} & - & \(TF\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of shield performance prediction models. thrust (in KN); and \(CT\) and \(PR\) can be found in Table 2[36; 37]. \(CD\) and \(ET\) are important characterizations of the geological conditions. \(CD\) affects the load of shield propulsion, while \(ET\) directly determines the strength of the soil and the difficulty of cutting, which is usually clearly documented at the stage of construction geological investigation and forms the basic data during construction. Ultimately, the input and output parameters of the model selected in this paper are shown in Table 2.  
\[PR=\frac{AS}{CS} \tag{1}\]  
\[SE=\frac{TF\cdot PR+2\pi\cdot CT}{\pi\cdot R^{2}\cdot PR} \tag{2}\]  
2. Data filtering  
Due to the complexity of the tunnelling environment, there is often a high level of volatility and noise in the tunnelling data, making it difficult to identify data patterns. The data were therefore grouped and filtered on the basis of the number of advancement rings. [PERSON]'s fences method was used to identify and filter noise data within each ring, preventing the negative impact of data fluctuations on the model's training [38]. This method is used to filter the observation value by setting the upper and lower limits of the data range, as shown in Equations (3)-(6). \[L_{\text{upper}}=Q3+1.5\times IQR \tag{3}\]  
\[L_{\text{lower}}=Q1-1.5\times IQR \tag{4}\]  
\[IQR=Q3-Q1 \tag{5}\]  
\[Ds= \begin{cases}\text{outliers}&\left(Ds<L_{\text{lower}}\text{ or }Ds>L_{\text{upper}}\right)\\ \text{non-outliers}&\left(L_{\text{lower}}<Ds<L_{\text{upper}}\right)\end{cases}  \tag{6}\]  
where \(Q1\) is the lower quarter quantile of the data, \(Q3\) is the upper quarter quantile, \(IQR\) is the interquartile range, \(L_{\text{upper}}\) is the upper limit of non-outliers, \(L_{\text{lower}}\) is the lower limit of non-outliers, and \(Ds\) is the data point in the selected area. 3.