## 5 Discussion  
Based on the prediction results from Tables 3 and 4, the models exhibited high degrees of agreement with actual values. For the six monitoring points, the MAE and RMSE values were within 6.7 mm, with minimums of 0.665 and 0.709 mm. The performance of R\({}^{2}\) is not consistent at each monitoring point, except WS0102; the R\({}^{2}\) value of other monitoring points is higher than 0.4, generally between 0.7 and 1, respectively. The shortest modeling time was 146.036 s, and the longest was 247.297 s. When comparing the two models under the RNN category, the GRU model outperformed the LSTM model in terms of accuracy. The RMSE values decreased by 22.1%, 18.4%, 6.8%, 14.2%, and 1.9%, while the MAE values decreased by approximately 24.2%, 25.2%, 3.8%, 27%, and 1.8%. The value of R\({}^{2}\) increases by about 12.4%. Additionally, modeling time was reduced by 34.4%. This suggested that GRU effectively maintained long-term dependencies and learned from extended time-series features. By simplifying the LSTM structure from three gates to two, GRU reduced parameters, accelerated convergence, and shortened runtime. During practical training, it improved prediction accuracy while accelerating the prediction process. Although the T-GCN model considered global spatial factors, the overall prediction accuracy did not significantly improve compared to the time-series network model, which only considered temporal dependencies. The introduction of adjacency matrices and feature matrices increased computational complexity and extended prediction time. This might be due to inaccuracies in calculating spatial distances or the simple Gaussian function used in the adjacency matrix, which may not adequately capture local deformations in landslides. During the construction of the complete graph for the monitoring points, the latitude and longitude of devices were converted to distances using the Vincenty algorithm, assuming the Earth is an ellipsoid, which could lead to discrepancies with actual distances. Furthermore, the method only estimated spatial correlation based on distance, which may not be precise, as landslides may experience local deformations that require additional constraints in the complete graph. Considering the lag effects of external factors such as rainfall and reservoir water levels, the T-GCN model prediction accuracy notably improved. RMSE values for the six monitoring points decreased by 51.8%, 50.7%, 51.1%, 9%, 39.6%, and 32.2%, respectively, and MAE values decreased by 50.8%, 59.9%, 53.9%, 17.0%, 32.4%, and 17.0%, respectively. However, the response of different monitoring points to external factors varied. At WS0102, WS0106, and WS0107, the inclusion of both rainfall and reservoir water level improved \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Model** & **WS0102** & **WS0103** & **WS0104** & **WS0105** & **WS0106** & **WS0107** \\ \hline \multirow{6}{*}{R\({}^{2}\)} & T-GCN & \(-\)4.138 & 0.704 & 0.979 & \(-\)5.115 & 0.467 & \(-\)4.138 \\ \cline{2-8}  & T-GCN (rain) & \(-\)1.450 & 0.865 & 0.501 & 0.959 & \(-\)8.467 & 0.636 \\ \cline{2-8}  & T-GCN & \(-\)1.262 & 0.928 & 0.911 & 0.983 & \(-\)2.861 & 0.750 \\ \cline{2-8}  & T-GCN & \(-\)0.212 & 0.877 & 0.956 & \(-\)1.228 & 0.756 & \(-\)0.212 \\ \hline \multirow{6}{*}{Modeling Time (s)} & T-GCN & 184.245 & 184.245 & 184.245 & 184.245 & 184.245 & 184.245 \\  & T-GCN (rain) & 247.297 & 247.297 & 247.297 & 247.297 & 247.297 & 247.297 \\ \cline{1-1} \cline{2-8}  & T-GCN & 196.010 & 196.010 & 196.010 & 196.010 & 196.010 & 196.010 \\ \cline{1-1} \cline{2-8}  & T-GCN & 235.985 & 235.985 & 235.985 & 235.985 & 235.985 & 235.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._prediction accuracy, while WS0103, WS0104, and WS0105 showed better results with only the reservoir water level as an external factor. This discrepancy could be due to the selection of the time window being based on qualitative analysis of the Dawuchang landslide's response to fluctuations in reservoir water levels and rainfall.