## 1 Introduction
Algorithms like ANFIS, (Adaptive Neuro-Fuzzy Inference System), were applied by [PERSON] and [PERSON] [38] to predict the FOS of slopes and they compared their results from the LEM method. The predictions made by the ANFIS model were acceptable for applications in slope stability prediction. In another study, [PERSON] et al. [39] utilized particle swarm optimization (PSO) to estimate the FOS of 3D slopes in comparison with a 3D finite element method (FEM) model using material properties (cohesion (c) and friction (\(\phi\)) and unit weight (\(\gamma\)) as input variables. They confirmed a successful application of PSO for 3D slope stability conditions but lower performance for 2D slope stability analysis. Artificial neural network (ANN) as a basic and benchmark AI model was used by [PERSON] and [PERSON] [36], [PERSON] [37], and [PERSON] [40], and its performance was studied to estimate slope stability compared to the LEM slope stability analysis. The results produced by the ANN model were found to concur with the results obtained by the LEM and allowed for the classification of sample observations according to the anticipated failure mechanism. In another study, [PERSON] [41] proposed a support vector machine (SVM)technique for the prediction of FOS and compared it with the ANN results. He found that the SVM was able to receive a slightly higher accuracy in comparison with the ANN technique. In addition, the same SVM model with different kernels, including polynomial, radial basis and spline, was proposed by [PERSON] [35] to classify the FOS of slopes. The accuracy of the model was proven to be very high as it showed 100% similarity when compared to the expected slope stability classification results. It was concluded that the classifications made by the SVM model were acceptable for applications in slope stability predictions; however, when the size of the dataset and/or the dimension of the input vector were high, the performance of the developed models was poor. In a study carried out by [PERSON] et al. [42], decision tree (DT) was used to predict the FOS of slopes and was compared with the results obtained by some other ML/AI techniques such as SVM. The accuracy of the DT model was proven to be acceptable, but it was lower than the SVM model. It is clear that the AI/ML models have enough potential in classifying/predicting slope failure or FOS. Table 1 presents some of the classifications/prediction studies in the areas of slope stability using AI/ML models. In these studies, FOS was set as model output where the model performance was assessed using the coefficient of determination (R\({}^{2}\)) and accuracy. In the light of the above discussion, it is clear that ANN and ANN-based models are the main body for the previous investigations. On the other hand, some other techniques, namely, tree-based, performed well in the areas of geotechnics and civil engineering [51, 52, 53, 54]. In this study, different classification systems are proposed for slope stability using decision trees (DT), random forest (RF), and AdaBoost tree-based techniques. As presented in Table 1, many researchers used key parameters (i.e., height (H), cohesion (c), friction (\(\phi\)), and unit weight (\(\gamma\))) for the classification of slope FOS under static conditions. According to