## 1 Introduction
The results indicate the presence of specific areas of instability within the simulated slopes, highlighting the effectiveness of correlation analysis in identifying likely failure zones. In previous studies on combining correlation analysis and numerical modeling, [PERSON]'s rank correlation coefficient measures the rank correlation of two variables nonparametrically. In landslide prediction, it has been used to investigate how parameters affect landslides. We used [PERSON]'s rank correlation coefficient to investigate slope angle, elevation, topographic roughness, curvature, and landslide frequency. These metrics had a moderate association with landslides, suggesting that they may be useful in predicting landslides [25; 26]. [PERSON]'s tau correlation coefficient is a nonparametric measure that quantifies the strength and direction of the association between two measures. No examples of its use in landslide prediction or geotechnical engineering were found in the literature [27]. It can be used, like [PERSON]'s rank correlation coefficient, to understand the relationships among the components of geotechnical difficulties [28]. Geotechnical engineering relies on numerical modeling to simulate and predict the performance of geological structures. The deformation and collapse of jointed rock slopes have been studied using numerical modeling [29; 30]. The combination of correlation analysis and numerical modeling can help explain landslide occurrence and slope stability. Landslide prediction models can be improved by using correlation analysis and numerical modeling based on displacement across multiple monitoring points in a slope to predict failure. The primary objective of this study is to develop and evaluate a novel approach for improving the accuracy of displacement-based landslide prediction. By combining sophisticated numerical modeling and advanced correlation analysis techniques, such as Spearman's rho and Kendall's tau, we aim to provide a more comprehensive understanding of deformation patterns and their relationship to slope stability conditions. The ultimate goal is to create more reliable predictive models and early warning systems to mitigate the risks and impacts of landslides. Correlation analysis shows a continuous and strong relationship between the majority of monitoring stations, suggesting a high degree of predictability in displacement patterns. This method goes beyond traditional visual evaluation by providing a quantitative and objective assessment of the progression of localized instability. In addition, correlation analysis enhances traditional stability indicators and provides a more thorough understanding of slope behavior. Although showing potential, additional physical validation and field calibrations are required before this technology can be used for operational early warning systems. However, the results of this study illustrate how improved correlation analysis can completely transform the prediction of landslides based on displacement. This opens up opportunities for the development of more accurate and efficient hazard mitigation solutions. The slope material properties used in this study were obtained from two sections of the Changde-Jishou Expressway in Hunan Province, China. The rationale for the selection of the monitoring points was to capture the critical locations and failure mechanisms of the retrogressive landslide. The monitoring points were placed at strategic locations along the slope profile, including the crest, middle section, and toe of the slope, to measure parameters such as displacement during the failure process. Figure 1 shows some structural collapse caused by landslides due to slope failure. The data presented in this paper are from numerical simulations using the FLAC 3D software version 7, rather than from direct field measurements. The FLAC 3D model was calibrated using laboratory test data, such as direct shear tests, to determine the appropriate micro-parameters for the soil and rock material behavior.