## 4 Prediction Accuracy and Efficiency Comparison Experiment
Using the displacement monitoring data from the six GNSS monitoring points deployed on the Dawuchang landslide, a complete graph of deformation monitoring was constructed as G = (V, E, W), where V = {V1, V2,..., VN} represented the nodes of the graph (GNSS monitoring points), and N was the number of monitoring points (Figure 8). A 7 \(\times\) 7 Gaussian similarity matrix A based on spatial proximity was established to represent the spatial correlation between monitoring points. The values in this matrix were computed using the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Model** & **WS0102** & **WS0103** & **WS0104** & **WS0105** & **WS0106** & **WS0107** \\ \hline RMSE & LSTM & 1.167 & 1.912 & 1.546 & 5.062 & 0.933 & **0.849** \\ (mm) & GRU & **0.909** & **1.561** & **1.441** & **4.343** & **0.915** & 0.880 \\ \hline MAE & LSTM & 0.945 & 1.370 & 1.013 & 3.348 & 0.722 & **0.665** \\ (mm) & GRU & **0.716** & **1.025** & **0.975** & **2.443** & **0.709** & 0.685 \\ \hline R\({}^{2}\) & LSTM & 0.175 & 0.915 & 0.916 & 0.974 & 0.402 & **0.871** \\ GRU & **0.501** & **0.943** & **0.927** & **0.981** & **0.425** & 0.861 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Displacement prediction accuracy of the GRU model and the LSTM model. Gaussian similarity function. The computed spatial relationship matrix is represented as A\({}_{\text{w}}\). The results are shown in Table 3.  
Next, the time-series deformation data characterization matrix Xt (7 \(\times\) 2524) was constructed for all monitoring points. Each row represented a monitoring point, and the corresponding column was the amount of time-series deformation at that point. Using the sliding window method, the displacement data were divided by a window size of 15 to form training and test sets of size 15 \(\times\) 7 \(\times\) 2005 and 15 \(\times\) 7 \(\times\) 490. The third step was model training while comparing the deformation prediction results considering rainfall and reservoir level lags and without superimposing external influences. When considering external influences when constructing the feature matrix, the external influences were selected in the window t + m instead of the attribute value corresponding to the original moment \(t\), and m was the empirical value. During the training process of the T-GCN model, the three hyperparameters of the learning rate, the number of training rounds (epoch), and the number of batch samples (batch size) were optimized using Bayesian optimization, and the final results were determined for the landslide prediction of Dawuchang. The overall T-GCN prediction model for the Dawuchang landslide was learning rate = 0.001, batch size = 128, and number of training rounds = 200. In addition, the number of hidden layer units was set to 64, and the ReLU activation function was used in the GCN module with the Adam optimizer and MSE least loss function. The specific prediction results on the set are shown in Figure 9 and Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{w_{ij}}\) & **WS0102** & **WS0103** & **WS0104** & **WS0105** & **WS0106** & **WS0107** \\ \hline WS0102 & 1 & 0.243558 & 0.000771 & 0.588207 & 0.192366 & 0.000749 \\ \hline WS0103 & 0.243558 & 1 & 0.108942 & 0.125867 & 0.537414 & 0.078851 \\ \hline WS0104 & 0.000771 & 0.108942 & 1 & 0.000354 & 0.037876 & 0.525511 \\ \hline WS0105 & 0.588207 & 0.125867 & 0.000354 & 1 & 0.310246 & 0.001102 \\ \hline WS0106 & 0.192366 & 0.537414 & 0.037876 & 0.310246 & 1 & 0.096883 \\ \hline WS0107 & 0.000749 & 0.078851 & 0.525511 & 0.001102 & 0.096883 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Side weightings of the Wushan County, Dawuchang landslide. Figure 8: Full connectivity map of the Wushan County, Dawuchang landslide.