###### Abstract  
This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: [PERSON]'s simplified method, the Fellerius method, [PERSON]'s simplified method, and [PERSON]'s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average _FS_ values were used to approximate the \"true\" _FS_ of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average _FS_ values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the _FS_ of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods. captional neural networks; slope stability; multiclass classification; regression; deep learning; landslides; limit equilibrium methods +
Footnote †: journal: Applied Sciences  
LEMs were explored extensively in the early days to study slopes with hand-performed computations due to the lack of computing power. Despite the long history, these methods evolved with the availability of computers as well. Computers enabled LEMs to consider the internal forces, pore pressure, and multiple layers of soils. In addition, the simplicity of the underlying theories contributed to the popularity and widespread use of these methods among engineers [5; 6]. However, LEMs are statically indeterminate problems, and the use of these methods requires assumptions of the internal forces that compromise their accuracy [7]. SRMs are the second category of methods that are commonly applied through the finite element, finite difference, and discrete element methods. These methods provide approximations to the exact solution of the governing equations of slope mechanics; therefore, they are more complicated than LEMs. Compared with LEMs, SRMs are advantageous in that they can consider strains [8]. Nonetheless, SRMs are relatively time-consuming, and their accuracy heavily relies on the accuracy of the considered geotechnical parameters [9]. Another group of approaches is LAMs, which make use of lower-bound and upper-bound theorems of plasticity [10]. While LEMs consider force and moment equilibrium along a specified slip surface, LAMs are rigorous. The reason for this is that, for the lower-bound, the stress field is in equilibrium with imposed loads at boundaries, while for the upper-bound, the velocity field solution is compatible with the imposed velocities. Despite the rigorousness, LAMs are not as popular as LEMs and SRMs due to the difficulties in constructing proper stress and velocity fields and obtaining optimal solutions. Additionally, the inclusion of pore water pressures, inhomogeneous soil profiles, and irregular slope geometries increase the complexity of LAMs. This can further complicate the manual construction of the stress and velocity fields, making LAMs impractical in most cases [11]. A more recent approach to slope stability is displacement-based analysis. This group of methods is focused on simulating the large movements and the post-failure behavior of slopes [12]. In many cases, the catastrophic damage caused by the deformations due to a landslide is more crucial than calculating the \(FS\)[13]. One of the most common methods in displacement-based analysis is the material point method (MPM) [14]. This method provides information for the internal development of shearing surfaces and the post-failure runout process [15].