## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Model** & **Input** & **Data Size** & **R\({}^{2}\)** & **Accuracy (\%)** \\ \hline \hline [PERSON] and [PERSON] [43] & DT & H, C, \(\phi\), \(\beta\), rainfall and water level data & 118 & - & 80 \\ \hline [PERSON] and & ANN, SOM & H, c, \(\varphi\), \(\beta\), \(r_{u}\) and \(\gamma\), \(k_{max}\) & 45 & 0.94 & \\ \hline [PERSON] and & & & & & \\ [PERSON] [37] & ANN & H, c, \(\varphi\), \(\beta\), \(r_{u}\) and \(\gamma\) & 46 & 0.95 & \\ \hline Lu and Rosenbaum [40] & ANN & H, c, \(\phi\),\(r_{u}\) and \(\gamma\) & 30 datasets & - & 99 \\ \hline Samui [41] & SVM & H, c, \(\phi\), \(r_{u}\) and \(\gamma\) & 46 datasets & 0.875 & - \\ \hline [PERSON] et al. [44] & DT & H, c, \(\phi\), \(\beta\) and \(\gamma\) & 6828 datasets & - & 72 \\ \hline [PERSON] et al. [7] & ANN & H, c, \(\phi\) and \(\gamma\) & 46 datasets & 0.982 & - \\ \hline [PERSON] [35] & SVM & H, c, \(\phi\), \(r_{u}\) and \(\gamma\) & 32 datasets & 1.0 & - \\ \hline [PERSON] and [PERSON] [38] & ANFIS & H, c, \(\phi\) and \(\gamma\) & 300 datasets & 0.980 & - \\ \hline [PERSON] et al. [45] & ANN & H, c, \(\phi\) and \(\gamma\) & 100 datasets & 0.99 & - \\ \hline [PERSON] et al. [46] & SVM & H, c, \(\phi\), \(\gamma\), rainfall data & 20 datasets & - & 88 \\ \hline [PERSON] [47] & ANFIS & H, c, \(\phi\), \(\beta\) and \(\gamma\) & 67 datasets & 0.952 & - \\ \hline [PERSON] and [PERSON] [48] & ANN & H, \(\beta\), \(\gamma\),\(c\) & 168 datasets & - & 96 \\ \hline [PERSON] et al. [49] & ANFIS & H, c, \(\phi\), \(\gamma\), and \(\gamma\) & 53 datasets & 0.96 & - \\ \hline [PERSON] et al. [10] & ANN & H, c, \(\phi\) and \(\gamma\) & - & 0.958 & - \\ \hline [PERSON] et al. [50] & ANFIS & H, c, \(\phi\) and \(\gamma\) & 30 datasets & 0.954 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some of the classifications/prediction studies in the areas of slope stability using AI/ML models. our review, there is a limited number of studies aimed at FOS estimation or status of stability classification under dynamic conditions. In the current study, the horizontal component of peak ground acceleration (PGA) is included in the input parameters. Therefore, the contribution of this study concerns, firstly, the use of tree-based models in slope stability classification, and secondly, the inclusion of a component related to dynamic conditions in slope stability. This allows for a more reliable slope stability classification under dynamic loading conditions. The rest of this paper is outlined as follows:  
Concepts of earthquake on soil slopes will be discussed in Section 2. Then, Section 3 describes the used models' concepts and fundamental facts. In addition, the same section will provide the needed information about data preparation used for modelling to the readers. Tree-based model developments for slope stability classifications will be provided in Section 4. The results of the study are evaluated and discussed in Section 5. In addition, the best tree-based model to classify slope stability will be discussed in the same section. Future work directions and the conclusion will offer some valuable input to the readers in Section 6.