## 1 Introduction  
Slope instability, as one of the most dangerous natural disasters, poses a great hazard to the protection of public property and life. How to evaluate slope stability and give corresponding engineering countermeasures is the top priority of slope engineering research [1]. The limit equilibrium methods commonly used in slope engineering include the Swedish arc method and the simplified Bishop method. Based on the Swedish method, the Bishop method considers the contribution of the force between the strips in the normal direction and calculates the safety factor by the average value of each strip. The overall safety factor is about 10% to 20% higher than that of the Swedish strip method [2, 3, 4, 5, 6]. [PERSON]'s method is often preferred when using the limit equilibrium method to analyze slope stability. For example, [PERSON] et al. [7] used the ISM method combined with on-site monitoring data to evaluate the slope state at different rainfall depths in real-time. The simplified Bishop method is simple and applicable, and its calculation results are accurate and computerized;therefore, it should be used first in the stability analysis of soil slopes without weak layers. The uncertainty of parameter values makes it difficult for the traditional reliability analysis to correspond with the practical engineering. To solve this problem, [PERSON] et al proposed an innovative method for stability analysis of jointed roclimass combined with interval values and block theory [8], and the microseismic load was further taken into consideration [9]. The interval non-probabilistic reliability analysis method is also applied to the stability analysis of tailings dam [10] and the real-time monitoring and pre-alarm for the disasters of tailings dam in mines [11]. As a common and accurate numerical simulation method for slope stability analysis, the finite element method is the first numerical simulation method applied to soil slope stability analysis [12]. A large number of studies [13; 14; 15; 16; 17; 18] have combined the finite element strength coefficient reduction method to analyze the slope stability problem, quantitatively revealing the mechanism and process of the progressive failure of the rock and soil mass before and after the instability, and calculating the safety factor of the critical slip surface. However, traditional numerical methods cannot provide accurate results of slope instability due to the complexity and uncertainty of multiple correlation factors and small unbalanced data samples. Based on the collected geotechnical properties and historical behavior of slope instances, the application of machine learning methods to evaluate slope stability has become an important solution [19; 20; 21; 22; 23; 24]. Neural networks are widely used in this field, including artificial neural networks [25], back Propagation Neural Networks [26], Differential Evolution Neural Networks [27], and other methods [28]. [PERSON] et al. [29] calculated the key sections of the landslide based on 3D modeling and obtained the factor of safety (FOS), which provided a reliable basis for slope stability analysis and comprehensive treatment. [PERSON] et al. [30] used the GBM method to establish the nonlinear relationship between the safety factor and influencing factors to carry out slope stability analysis. [PERSON] et al. [31] built different support vector regression (SVR) kernels to predict FOS values and the results show that the radius basis function (RBF) kernel could produces more accurate prediction performance. [PERSON] et al. [32] established six integrated artificial intelligence models to predict the slope stability and proved that the integrated AI methods have great potential for slope stability prediction.