###### Abstract
The MPM has been used in numerous research projects and case studies. For example, [PERSON], et al. [16] used centrifuge tests to assess the effect of subsoli stiffness on the failure mechanism of dykes. [PERSON], et al. [17] utilized the MPM to investigate the runout process of the Maierato landslide. Despite the clear underlying theories, the above categories of physics-based methods require assumptions to deal with the spatial variability of earth materials and their complex geotechnical behavior [18]. Moreover, the accurate implementation of these models to represent real-world conditions is time-consuming, computationally expensive, and requires a high level of expertise. Consequently, these models are either complex and impractical or over-simplified and inaccurate for field engineers when analyzing real-world problems. Additionally, these conventional methods are unable to take advantage of the large volumes of available data (\"big data\"), especially image and video data, and recent advancements in artificial intelligence. Recently, a successor of artificial neural networks (ANNs), called convolutional neural networks (CNNs), gained popularity as a successful deep learning network. CNNs attracted an increasing amount of attention following their success in classifying 1.2 million high-resolution images into 1000 different categories [19]. These networks can significantly reduce model parameters and automatically extract data (image) features, and hence they generated remarkable improvements in learning outcomes [20]. Despite the success of CNNs in computer vision, they have only been explored in a few civil engineering applications [21]. Specifically, deep learning with CNNs has been used in structural health monitoring and vibration-based structural damage detection [22; 23; 24; 25; 26]. In geotechnical engineering, CNNs enabled researchers to use raw data and field records for assessing liquefaction potential [27], monitoring and predictinglandslide displacement [28; 29], identifying the source location of microseismic events in underground mines [30], predicting the spatial correlation coefficients from cone penetration test data [31], and analyzing landslide susceptibility [32; 33]. Despite this progress, the application of CNNs in classical geosystems such as slope stability analysis is still rare. As far as we know, the earliest and the only attempt is the authors' effort of using a CNN multiclass classifier for obtaining the \(FS\) of slopes, which was validated against [PERSON]'s simplified method [34]. In this study, deep learning models are combined with physics-based models of slope stability to obtain new models for slope stability analysis. In particular, a multiclass classification model and a regression model were employed to automatically predict the \(FS\) of slope images. The performance of both models was evaluated against four widely adopted LEMs in terms of accuracy and computing efficiency: the ordinary method of slices (OMS), [PERSON]'s simplified method (BSM), [PERSON]'s simplified method (JSM), and [PERSON]'s corrected method (JCM). The rest of the paper is organized as follows. Section 2 presents a brief introduction to the basics of deep learning with CNNs, followed by the theories of the proposed deep learning models. Next, Section 3 describes a procedure proposed for generating slope image data with different geometries and soil properties, as well as the labeling and pretreatment of the slope image data. The four LEMs employed in this study, including their theories and implementations, are detailed in Section 4. In Section 5, the results for training, validation, and testing are presented and analyzed, and conclusions are reached based on them.