## 1 Introduction  
Landslides have been classified as one of the most hazardous natural events, causing damage to lots of public and private properties, and even human lives. Many countries spend a considerable amount of money annually to mitigate and control these events. To do this, one of the most straightforward and conventional steps is identifying potential landslide sections by conducting slope stability analyses. A number of parameters affect the stability of a slope; the most important ones are the geometrical shape of the slope, its gravity, the shear strength of geomaterial, and the water quantity in the soil. Generally, the above-noted parameters are represented by five different factors: the slope height, the pore-pressure ratio, the slope angle, the soil unit weight, and the soil shear strength parameters (i.e., cohesion and angle of internal friction) [1]. These parameters bring lots of uncertainties to the problem, which makes slope stability analysis a statistically indeterminate problem with a high non-linearity. Different researchers have proposed different assumptions with the aim of simplifying this problem, which has resulted in the development of several methods for slope stability analysis [2; 3]. Typically, the stability of a slope is expressed by the term 'factor of safety' (FOS). If the FOS value is greater than one, the slope is thought to be safe. To compute the FOS value, several methods have been suggested in different studies. Two conventional types applied for this purpose are numerical methods (such as finite element method, finite difference method, and discrete element method) and limit equilibrium methods (LEMs) [4; 5]; both have certain advantages as well as drawbacks. Finite element methods are used to analyze slope stability for two-dimensional and three-dimensional simulation. However, each method has its advantages and disadvantages. Two-dimensional methods are usually conservative and can differ by up to 30% from three-dimensional results. This creates unsafe designs for FOS projects. On the other hand, 3D models have a higher execution time, which also reduces their use [6; 7]. In general, these traditionally used methods are complex and iterative and cause an overloaded computation system [8]. Such faults have obliged researchers working in this field to make attempts to propose alternative approaches to slope stability computation. In recent years, a number of studies have used soft computing techniques (e.g., vector machine algorithm, neural networks, etc.) to solve such highly complicated, multivariate, non-linear problems. In various fields of science and engineering, intelligent methods have proven to be highly efficient in function approximation [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Different studies have developed various models based on artificial neural networks (ANNs), support vector regression (SVR), and random forest techniques to solve such complex problems [33; 34; 35; 36; 37; 38; 39]. Several researchers have employed these methods to work on the slope stability problem [1; 3; 8; 40; 41]. ANN and SVR essentially predict the relationship between the input(s) and output for a certain set of data. These methods include various strengths and weaknesses, which can be improved with optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and ant colonization optimization (ACO). Optimization algorithms are used to optimize various problems. Complex problems can be simulated well using a combination of different models. Table 1 summarizes some of the past proposed works in the area of slope stability using soft computing techniques. In this research, a simulation was implemented to evaluate FOS using an SVR model. In this simulation, different models were designed based on four kernels.