## 6 Conclusions and Future Works  
To achieve the aim of this study, tree-based models including DT, RF, and AdaBoost were developed to classify the stability of 700 slopes (464 safe slopes and 236 unsafe slopes) under seismic condition, which were modelled and analysed in GeoStudio software. The variables of H, \(\beta\), C, \(\phi\), and PGA were set as model inputs for the classification of slopes where FOS \(\geq\) 1 and FOS \(<\) 1 was considered for safe and unsafe slopes, respectively. To measure the performance of the DT, RF, and AdaBoost models, accuracy, precision, recall, F1-score, and AUC as performance indices were calculated for both stages of training and testing. After conducting modelling procedures of classification, the best technique was selected based on the performance indices' results. From the training part, it was found that the AdaBoost was a perfect technique capable of achieving the highest possible performance compared to the other employed models. Additionally, a higher degree of classification performance for the testing phase was reported for all calculated indices except AUC. Values of 0.910, 0.931, 0.931, 0.931, and 0.931; 0.961, 0.914, 0.915, 0.916 and 0.914; and 0.968, 0.891, 0.895, 0.908 and 0.891 were obtained for AUC, Accuracy, F1, Precision, and Recall of AdaBoost, RF, and DT models, respectively. These values confirmed the successful use of tree-based models in classifying slope stability. However, the better performance and higher capability for classification purpose goes to the proposed AdaBoost technique. Therefore, it can be introduced as a new technique for slope stability classification with the largest number of matched cases. It is well established that to propose a new method for classifying slope stability cases using AI techniques, extensive investigation is required. Therefore, in order to develop a model for classifying slope stability, a comprehensive database comprising real cases must be gathered and utilized. Yet, collecting such database is very difficult and time consuming. By providing the mentioned data, slope stability classifications can be conducted using new (hybrid) AI techniques, such as RF or AdaBoost, combined with metaheuristic algorithms. Moreover, the use of real slope stability data based on different types of soils considering other properties, such as unit weight, permeability, and ground water table, would be of interest and importance to geotechnical engineers. In this regard, model generalization as an important issue in classification and prediction problems can be considered, with the \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Performance Indicators**} & \multicolumn{6}{c}{**Rank**} \\ \cline{2-11}  & **AUC** & **Accuracy** & **F1** & **Precision** & **Recall** & **AUC** & **Accuracy** & **F1** & **Precision** & **Recall** & **Total** \\ \hline DT & 0.968 & 0.891 & 0.895 & 0.908 & 0.891 & 3 & 1 & 1 & 1 & 1 & 7 \\ \hline RF & 0.961 & 0.914 & 0.915 & 0.916 & 0.914 & 2 & 2 & 2 & 2 & 2 & 10 \\ \hline AdaBoost & 0.910 & 0.931 & 0.931 & 0.931 & 0.931 & 1 & 3 & 3 & 3 & 3 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Modelling results for the testing datasets of DT, RF, and AdaBoost for slope stability classification. Figure 10: Chart of results obtained from the models compared to expected results. developed models covering a wider range of input parameters, as well as a larger number of effective problem variables. Conceptualization, D.J.A., B.G. and P.G.A. ; methodology, D.J.A., F.I.M.R., M.K. and P.G.A. ; software, D.J.A., F.I.M.R., M.K. and P.G.A. ; formal analysis, D.J.A., F.I.M.R., M.K. and P.G.A. ; writing--original draft preparation, D.J.A., F.I.M.R., M.K., P.G.A., B.G., P.C.R. and M.F. ; writing--review and editing, D.J.A., F.I.M.R., M.K., P.G.A., B.G., P.C.R. and M.F. ; supervision, D.J.A., B.G. and P.G.A. ; Data curation, B.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.