## 4 Results  
### Single-Point Prediction Results  
Three monitoring points were randomly selected within the landslide area to evaluate time-to-failure predictions using their velocities. Figures 6-8 display the velocity distributions and inverse velocities for these points under three data-processing scenarios (Cases 1-3). As illustrated in Figures 6-8, the velocity anomalies identified in Figure 4 persist across all cases, with inverse velocity plots exhibiting heightened sensitivity to these deviations. Distinct slopes of the inverse velocity fitting lines, such as those in Figures 6d and 7d, reflect spatial heterogeneity in pre-failure deformation dynamics.  
Table 2 summarizes prediction errors (difference between predicted and actual failure times) for all three points across the data-processing scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Errors (h)** & **Case 1** & **Case 2** & **Case 3** \\ \hline Point 1 & \(1.98\) & \(-1.93\) & \(2.85\) \\ Point 2 & \(-3.21\) & \(-3.97\) & \(-0.73\) \\ Point 3 & \(-3.84\) & \(-1.92\) & \(-3.47\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landslide prediction errors.  
Figure 6: (**a**) Velocity curves of different points (**b**) Velocity and inverse velocity of point 1 (**c**) Velocity and inverse velocity of point 2 (**d**) Velocity and inverse velocity of point 3. The pink line represents the time when the landslide occurred. The green line represents the position where the inverse velocity is 0.  
As summarized in Table 1, prediction errors across the three monitoring points under all three operational scenarios predominantly range from \(-4\) to \(+3\) h. To evaluate the robustness and reliability of these outcomes, comparative error distributions were analyzed for forecasts generated at ten distinct temporal nodes during the final 10 h pre-failure phase (Figure 9).  
Figure 9 illustrates the error distributions of failure time predictions across three monitoring points under Cases 1-3. Key observations include: (1) Prediction errors for all cases largely span \(-5\) to \(+5\) h, with Case 1 and Case 2 exhibiting pronounced variability at Point 1; and (2) Points 2 and 3 demonstrate relatively consistent errors (\(>\)2 h) across scenarios, though concurrent occurrences of premature (negative errors) and delayed (positive errors) predictions persist. This inconsistency, particularly the coexistence of over- and under-predictions, undermines definitive failure time determination, highlighting the necessity for developing more stable and reliable prediction methodologies.  
Figure 7: (**a**) Velocity curves of different points (**b**) Velocity and inverse velocity of point 1 (**c**) Velocity and inverse velocity of point 2 (**d**) Velocity and inverse velocity of point 3. The pink line represents the time when the landslide occurred. The green line represents the position where the inverse velocity is 0.  
Figure 8: (**a**) Velocity distribution of different points (**b**) Velocity and inverse velocity of point 1 (**c**) Velocity and inverse velocity of point 2 (**d**) Velocity and inverse velocity of point 3. The pink line represents the time when the landslide occurred. The green line represents the position where the inverse velocity is 0.  
Figure 9: (**a**) Error distribution of landslide prediction time at 10 different moments for 3 spatial points under 3 cases. (**b**) The statistical chart of (**a**).