## 4 Prediction Accuracy and Efficiency Comparison Experiment  
Horizontal displacement was predicted, and two sets of comparative experiments were designed. The first group of experiments focused on the accuracy and efficiency of local slope displacement prediction, comparing the time-series prediction results of two models, LSTM and GRU. Based on the results from the first group, the model with better prediction accuracy was selected for the second group of experiments. This second group considered the overall spatial correlation of the landslide to construct the T-GCN deep learning prediction model. This model was then compared with the deep learning prediction method of T-GCN augmented by external influencing factors. The first group of experiments used displacement deformation time-series data from six monitoring points on the Dawuchang landslide to build the prediction model. The dataset was divided into training and testing sets in an 8:2 ratio. The strategy involved using the displacement data  
The two prediction models exhibited a good agreement with the actual deformation trends at the six monitoring points; however, both exhibited certain latency. The LSTM model at the WS0107 monitoring point was slightly more accurate in terms of RMSE, MAE, and R\({}^{2}\) indicators, while for other monitoring points, the GRU model predictions were closer to the actual values, as shown in the Table 2, bold data indicate the model with the best performance of the evaluation indicators. The modeling times for the LSTM and GRU models were 222.745 and 146.036 s, respectively. Regarding efficiency, the GRU model significantly reduced the modeling time at seven monitoring points, decreasing the time by 34% compared to the LSTM model. Therefore, in the second group of experiments, the GRU model was selected for training in time-series feature learning. To further assess the effectiveness of the proposed approach, we systematically compared the T-GCN model with several classical methods, including Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and physics-based models. The results showed that traditional statistical models like ARIMA struggled to capture the nonlinear responses of landslide deformation to external drivers, resulting in significantly higher prediction errors. While simpler machine learning models (e.g., SVR and Random Forest) outperformed ARIMA to some extent, they still exhibited considerable errors, particularly at complex deformation sites such as WS0105. Physics-based models performed well under steady-state creep conditions but were unable to characterize sudden or transient deformation events. In contrast, the T-GCN model consistently demonstrated superior predictive accuracy, effectively capturing rapid deformation episodes, spatially correlated displacements (e.g., WS0103-WS0104), and deformation responses driven by rainfall and reservoir fluctuations. Nonetheless, these advantages come with the trade-off of higher computational costs and greater data demands compared to conventional approaches. In the second group of experiments, the spatial correlation between monitoring points was first analyzed. A complete graph of the Dawuchang landslide deformation monitoring network was established, and a Gaussian similarity matrix was created. Next, a feature matrix for the time-series deformation data was constructed to predict deep learning deformation using the T-GCN. Additionally, considering the lag effects of rainfall and reservoir water levels, external influencing factors were incorporated by selecting a time window t+m to replace the original attribute values corresponding to time t, and the deep learning prediction method of T-GCN with external influencing factors was employed. First, the spatial correlation between monitoring points was calculated.