## 4 Homogeneous Soil Slope (Simple Slope) Stability Analysis Using the FLAC3D Model
### Basic Statistical Properties of Simple Slope  
By analyzing each monitoring point separately from Table 2, the study recognizes the heterogeneity of slope materials and conditions, resulting in more localized and relevant predictions. The different characteristics between the reduction factor and displacement at each monitoring point highlight the complexity of landslide-triggering mechanisms [46; 47; 48]. As noted in the analysis, some points show little to no discernible relationship, while monitoring point 2 indicates a potential trend. The minimal displacements and lack of clear correlations at points 1, 3, 4, and 5 suggest that these locations on the slope are likely stable, with marginal changes occurring even under varying stability conditions [48]. This is consistent with [PERSON] et al., who found that certain areas of a slope can remain unaffected while other sections fail [49]. In contrast, point 2 shows more variability in displacement measurements, which may signal the onset of local ground movement. As discussed by [PERSON] et al., the initiation of slope failure is complex, but increasing deformations suggest changing levels of stability that may precede eventual mass movements [50; 51]. The spread in the point 2 data may reflect this early development of failures from additional weight higher up the slope. Overall, these variable strengths and even directions of correlations highlight the heterogeneity of landslide-triggering mechanisms within a slope [49]. Rather than oversimplifying the complexity, site-specific monitoring provides localized insights to better predict zone-specific stability and movement. Targeted early warnings, rather than general alerts, can then be issued when monitoring identifies distinct unstable regions.  
Figure 7: Displacements vs. reduction factor for the existence monitoring points of soil slope.  
Calculating summary statistics such as mean, standard deviation, minimum, and maximum provides quantitative insight into the distribution and spread of displacement readings at each monitoring point [52]. As discussed by [PERSON] et al., the mean displacement indicates central tendency, while the standard deviation measures the amount of variation between measurements. A key observation is that monitoring points with higher standard deviations have greater variation in displacement, which correlates with changes in the stability reduction factor [53]. As [PERSON] and [PERSON] explain, increased variability may indicate the early development of movements arising from developing instability. Thus, the statistical dispersion captures subtle effects that are missed by analyzing only the averages [54]. The maximum and minimum displacements also reveal the full dynamic range of variability in the measurements [49]. Defining these endpoints helps to characterize the magnitude of the overall movement currently occurring, even when most measurements are clustered around low means.  
Further analysis of exceedance rates and significance tests can build on these descriptive metrics [48]. This statistical characterization provides a quantitative method for systematically tracking changes within slope monitoring data. Rather than just visual assessments, it allows for more definitive comparisons of which sites exhibit the greatest variability, indicating areas of increased risk [54]. The novelty lies in the addition of detailed statistical performance metrics to traditional stability correlations.