## 6 Conclusions  
The integration of advanced statistical correlation analysis into slope stability studies represents a significant leap forward in the field of landslide prediction. This novel approach not only improves the accuracy of predictions but also provides deeper insights into the underlying mechanisms of slope failure. As this methodology continues to evolve, it holds the promise of significantly reducing the risks associated with landslides, thereby protecting lives and infrastructure. The following are the key conclusions:  
1. For the homogeneous soil slope model, the results showed [PERSON]'s rho correlation coefficients ranging from 0.31 to 0.76, and [PERSON]'s tau values ranging from 0.29 to 0.64, indicating variable displacement-stability relationships across the monitoring points. In contrast, the joint rock slope model exhibited strong positive total displacement correlations, with [PERSON]'s and Kendall's coefficients showing ranges close to \(+1.0\) and \(-1.0\) at most monitoring points. The maximum mean horizontal and vertical displacements reached 44.13 mm and 22.17 mm, respectively, at the critical point 2.
2. The quantitative correlation analysis allowed the researchers to identify and distinguish between stable and unstable zones on the simulated slopes, providing localized insights into the progression of slope failures. For example, monitoring point 2 on the soil slope showed a mean horizontal displacement of 17.65 mm and a mean vertical displacement of 9.72 mm under stability reduction, indicating it as a critically unstable location.
3. By quantifying the strength and directionality of the correlations between displacement measurements and stability reduction factors, the methodology enables more precise identification of areas prone to instability and potential failure. This level of detail can inform targeted remediation efforts and early warning systems, which is a significant improvement over generalized assessments of overall slope stability.  
Figure 18: Correlation coefficients for total displacement of the joint rock slope.  
Future work should focus on further refinement of the proposed correlation analysis methodology through additional physical model testing and field calibration prior to implementation for early warning systems.  
Methodology, H.L.; Validation, I.H.U., H.L. and J.I.H.; Formal analysis, I.H.U.; Writing--original draft, I.H.U.; Writing--review & editing, I.H.U., H.L. and J.I.H. All authors have read and agreed to the published version of the manuscript.  
This paper gets its funding from Guizhou Provincial Major Scientific and Technological Program (2023-425); Project (NRMSSHR-2022-Z08) supported by Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources.  
Not applicable.  
Patient consent was waived due to REASON (please provide a detailed justification).  
The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.  
The authors are very thankful to the financial support of the Guizhou Provincial Major Scientific and Technological Program (2023-425); Project (NRMSSHR-2022-Z08) supported by Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources.  
The authors declare no conflict of interest.