## 1 Introduction  
Geotechnical engineers often employ analytical and empirical methods in order to estimate the safety factor, based on design parameters and engineering properties, of soil or rock material. It is a challenging task to develop an adequate model to efficiently simulate site specific engineering geological conditions and follow the appropriate design approach in order to eliminate the possibility of failure and propose the most cost-effective design. Slope stability analysis is a standard practice in geotechnical engineering employed for the estimation of the stability of natural or man-made slopes such as embankments of highways, railways, earth dams, tailings, etc. The analysis of slope stability mainly involves the calculation of the factor of safety (FOS), which is defined as the ratio between shear strength and the acting shear stress. The key parameters that define the geometry of the slope (i.e., height and slope inclination) and the material properties (i.e., angle of internal friction, cohesion, and pore water pressure) influence the evaluation of stability of slopes [1; 2; 3]. Many sources of uncertainties, such as soil properties and loading, contribute to the stability of a slope [4; 5; 6]. The slopes can be classified as stable slopes (SS) or unstable slopes (US), depending on whether their FOS is greater or less than one [7]. The assessment of slope stability is usually performed using analytical techniques, such as the limit equilibrium method (LEM) and finite element methods. The challenge associated with further development of slope stability analysis methods is to create a reliable generic design tool in order to perform precise evaluations of slope performance. Before the advent of computers, slope stability analysis was conducted using semi-graphical solutions, using manual calculations, or using stability charts [8]. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. Geotechnical software based on analytical methods such as the limit equilibrium method (LEM) are widely used by engineers although this method is known to be inadequate when analysing complex slope conditions, requiring more efficient designs, where more sophisticated tools like finite element methods are used [9]. Statistical methods for slope stability classification are based on mathematical formulas that are used in the statistical analysis of research. Multiple regression is a statistical analysis method that can predict the nature of relationship among independent variables and dependent variables. Multiple regression is able to predict the relationship of multiple independent variables against an output variable. This technique is widely used in analysing slope stability problems [10]. For instance, [PERSON] and [PERSON] [11] used multiple regression to predict the FOS of homogeneous slopes. The cohesion of soil (c), angle of internal friction (\(\phi\)), unit weight of soil (\(\gamma\)), and seismic coefficient (k) were used as input parameters, and the output parameter was FOS. It was concluded that the predictions made by the multiple regression model were acceptable. In a similar study, [PERSON] and [PERSON] [12] used the height of cut or slope height H, material properties, cohesion (c), friction (\(\phi\)), slope inclination (\(\beta\)), unit weight (\(\gamma\)), and dimensionless parameter (m) as input parameters to predict the status of stability. They also reported a very similar conclusion to the study by [PERSON] and [PERSON] [11]. However, the analyses performed by statistical models are only statistical-based, and they are not able to provide a clear view to researchers and designers [13]. Artificial intelligence (AI) and machine learning (ML) techniques have been successfully implemented in the area of engineering and sciences [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] for the last 25 years. The same models were used to solve the slope stability problems [3; 33; 34; 35; 36; 37].