## 1 Introduction  
Predicting landslides remains a challenge in the fields of civil engineering, mining, and environmental protection because slope stability is of paramount importance [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. The safety factor is a critical parameter in slope design. Methods for slope stability analysis can be divided into three main categories: kinematic analysis, limit equilibrium analysis, and rockfall simulators [12; 13]. Firstly, methods based on the principle of limit equilibrium: These methods assume that the slope is close to collapse and that the forces acting on the potential failure surface are in equilibrium. Some examples of these methods are the Fellenius, Bishop, Spencer, and Morgenstern-Price approaches [14]. Secondly, methods based on observation and experience for the analysis of rock slopes: Approaches such as SMR (Slope Mass Rating) and Q-Slope are based on the correlation between slope stability and geological characteristics [15]. Finally, numerical methods include three different modeling approaches: continuum, discontinuum, and hybrid. The finite element method (FEM) and the finite difference method (FDM) are both examples of continuum modeling [16; 17]. The slope safety factor is calculated numerically using the Strength Reduction Method (SRM). It gradually weakens the soil until the slope fails. The SRM can account for maximum and residual material strength, improving the representation of slope behavior [18]. Suitable for complicated shapes and materials, the SRM requires more data and computational resources than limited equilibrium techniques [19]. A single safety factor is difficult to determine due to the lack of uniqueness of the failure mechanism. Three criteria are used to determine slope failure using the SRM [20]: (1) Sudden genetic change: As the reduction coefficient decreases, the displacement at slope monitoring sites changes rapidly. This tendency predicts instability and helps explain slope deformation. (2) Computational convergence failure: During the solution search, the calculation may not reach a stable state, indicating failure. However, the numerical technique can change the reliability of this criterion. The connection of the plastic zone within the slope body is a key indicator of collapse. This criterion is not universal due to material properties such as Young's modulus and Poisson's ratio. (3) Abrupt displacement variation is a promising strategy. By monitoring specific slope body locations, researchers have found dramatic displacement changes as the reduction coefficient increases. This finding confirms slope instability better than non-convergence or plastic zone coupling. Slope failures are a widespread and devastating natural phenomenon, causing approximately $153 billion in damage worldwide each year [21]. Although there have been improvements in geotechnical engineering, accurate prediction of slope instability based on displacement is still a difficult task [22]. The main reason for this is the complex interaction of elements such as soil or rock composition, climatic conditions, and human activities, all of which can lead to slope instability [23; 24]. To overcome these limitations, this paper presents a new method that combines sophisticated correlation analysis with numerical modeling to improve the accuracy of landslide prediction based on displacement. The current methodology uses correlation analysis, specifically [PERSON]'s rank correlation coefficient (rho) and [PERSON]'s tau correlation coefficient, to measure the relationships between displacement data from multiple monitoring points for both soil and rock slopes that are experiencing a gradual decrease in stability. This methodology provides a more complete understanding of deformation patterns and their correlation to slope stability conditions. Statistical tests are used to examine the relationships between displacement distributions and stability parameters.