# Comparative Analysis for Slope Stability by Using Machine Learning Methods
## 1 Introduction  
Slope stability is a crucial representation of rock, soil, or combined mass under various failures that lead to moving removable masses on a slope downstream [1]. Slope failures lead to ground deformation on a different scale and cause damage to facilities, roads, railways, infrastructures, and foundations [2, 3]. The instabilities occurred under certain conditions or triggering factors, which can be related to the slope mass geometry, stress-strain history, geo-material status, external loadings, regional climate, seismic activity,-pore water pressure conditions, and geo-structures. These triggering factors are used to determine the behavior of sliding mass and the potential slip surface expansion in slopes [4]. Slope mass geometry, or slope topography, can provide favorable conditions for slope mass sliding, falling, and overturning. In general, the likelihood of sliding rises as the slope angle gets steeper [2]. The presence of stress-strain history in a slope can cause ground movements, such as plastic slips and creep, which are mainly a time-dependent behavior of loose soils. Geo-material status is one of the well-known conditions that might provide a suitable condition for slope instabilities. The existence of loose layers or clay lenses, organic soils, marl, and glacial deposits are always considered essential drivers in slope failures [1]. External loadings and seismic activities can trigger the slope to failure under dynamic conditions, especially in an earthquake event, liquefaction, sand boils, dewatering of dams, tunnel boring, or heavy machinery. Regional climate responsibility for slow changes in slope conditions is known as weathering. Weathering can be caused a decrease in the shear strength and durability of soil grains, which is considered a trigger for sliding in the slope [5,6]. Pore water pressure is one of the well-known motivating factors that leads slopes to failure. Most stability analysis equations consider the pore water pressure as one of the critical parameters in their calculations [1]. Pore water pressure is the pressure of groundwater held in soil (or rocks) at the pores between particles, increasing the fluidization of particles and reducing soil interparticle friction [4]. Finally, geo-structures are mostly related to faults, folding, and complex geological deformations that make ground changes. These structures provide appropriate conditions for particular failures such as debris, slope toe erosion, or side-base instabilities. Although many of these failures are related to rock slopes, numerous cases have also been observed in earth slopes [7,8]. In slope stability analysis, the slip surface that is considered as keyline between stable and unstable masses in a slope can be calculated based on the safety factor (F.S) index [1]. Regarding the geological composition of slopes, failures can be classified as earth slope and rock slope instabilities. Earth slopes act as homogeneous masses, which leads to the generation of the rotational (or massive failure) and planar forms. In rock slopes, the failure types are more varied, and the slope condition can be categorized as planar, wedge, toppling, and rotational failures [9,10]. Regardless of the type of failure that occurs in slopes, using the possible slippery mass condition can lead to the calculation of the F.S for the slope. F.S can be estimated by closing the polyhedral forces' vectors or moments in an equilibrium state at the assumed sliding surface regarding dynamic and/or static conditions for two- and/or three-dimensional spaces [4]. \[\text{F.S}=\frac{\sum\text{Resistance forces or moments}}{\sum\text{Activation forces or moments}} \tag{1}\]  
If all assumptions and requirements are met, and these polyhedral force vectors are closed, the slope mass must be in equilibrium, and the stability analysis must be reliable. The non-closure polyhedral vectors (forces and/or moments) are exemplified by the failure to satisfy several key requirements [9].