###### Abstract  
Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the ANN, the particle swarm optimization (PSO) algorithm, and the principal component analysis (PCA) method, a novel PCA-PANN model is proposed. Then, a dataset of 307 slope cases covering a wide range of slope geometries and mechanical properties of geomaterial is developed. The hybrid machine learning model trained with the dataset is applied to the factor of safety (FoS) prediction of the actual slope, and three evaluation indicators are introduced to measure the prediction performance of the model. Finally, the sensitivity analysis of input parameters is carried out, and the slope protection strategy for different sensitive factors is proposed. The results show that this new model can quickly obtain the FoS and stable state of the slope without complex calculation, only by providing the relevant characteristic parameters. The correlation coefficient of the PCA-PANN model for slope stability analysis reaches more than 0.97. The sensitivity degree of influencing factors from large to small is slope angle, cohesion, pore pressure ratio, slope height, unit weight, and friction angle. slope stability; factor of safety; principal component analysis; machine learning; neural network +
Footnote †: journal: Medical  
include limit equilibrium methods (LEM) and qualitative evaluation methods. The LEM was first proposed by [PERSON] [16] and is the earliest and most widely used method in slope stability research. The principle of LEM is to evaluate the slope stability by calculating the FoS based on the relationship between the shear strength and sliding force on the slope sliding body [17]. Commonly used LEM include the Swedish arc method, Morgenstern-Price method, simplified Bishop method, [PERSON]'s generalized method, [PERSON] method, etc. [18; 19; 20; 21]. In addition, [PERSON] and [PERSON] [22] used a quasi-dynamic method to establish a strict LEM considering interstrip forces to automatically search the sliding surface of a 3D landslide and determine its FoS. Recently, based on the LEM, [PERSON] and [PERSON] [23] used the GeoStudio software to study the slope safety factor in Kalimpong, Darjeeling Himalayas under dry and saturated conditions. Apart from LEM, a series of excellent numerical analysis methods, such as finite element method (FEM), finite difference method (FDM), and Fast Lagrangian Analysis of Continua (FLAC) emerged [24], which can effectively solve complex slope stability problems. Numerous studies combined with numerical analysis methods calculated the FoS, and quantitatively revealed the mechanism and process of gradual failure of geomaterials before and after instability. The above studies greatly deepened the understanding of slope instability. The slope is an open, uncertain, and nonlinear complex system. Affected by a variety of random and uncertain factors (e.g., the spatial variability of geotechnical parameters and the infiltration of water), the application of the above methods was limited by several restrictions, and it is difficult to achieve the ideal prediction effect [25; 26; 27]. For example, due to the large number of potential sliding surfaces, it is cumbersome and difficult to find the critical sliding surface by using the LEM [28; 29]. Therefore, the use of LEM is not feasible when a significant amount of slope cases need to be analyzed in a limited time, particularly for the rapid detection of landslide activity and issuing landslide warnings [30; 31]. The accuracy of the numerical simulation method depends on the setting of the constitutive model, boundary conditions, and mechanical parameters, which often require rich engineering experience and in situ back analysis to obtain reasonable results [32]. Moreover, compared with the LEM, the numerical simulation method requires longer time [33].