# Comparative Analysis for Slope Stability by Using Machine Learning Methods
## 4 Results and Discussion
The lowest overall accuracy belongs to DT, with AUC = 0.812. In this regard, it is possible to say that the application of MLP can provide reliable and accurate results for F.S with good agreements with LEMs. Using such machine learning algorithms can help to develop an optimized method to understand the stability condition of earth slopes and suggest appropriate stabilization techniques to stabilize the slope. Other benefits of applying such techniques are reducing the cost of surveys, reducing calculation time, and increasing accuracy in complex stability analysis. Using these advantages helps geo-engineers to proceed with the stability analysis in the initial time of earthwork and the conduction of on-time stabilizations. Compared to traditional methods, using machine learning techniques to develop an accurate prediction of F.S has a number of advantages and limitations. An advantage of the predictive models offered in this study is that they predicted a suitable and trustworthy estimate of F.S that could be applied to an analysis of the stability of an earth slope. Additionally, the comprehensibility and quickness of the achievements for prediction and classification have made it possible for users and personnel with low experience to use it well. Increases in the estimation's accuracy for predicted outcomes can be presented by lowering error rates capable of being implemented in less time. An attitude towards the achievements of this research can state that the use of machine learning algorithms (mentioned classifiers) for predictions is independent of the sliding surface types and related to the input-output parameters for each stability class. Thus, it is possible to develop an extension method for other slope instabilities such as rock falls, shallow movements, debris, and toppling. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Classifier** & **Maximum Loss** & **Minimum Loss** & **Mean Loss** \\ \hline MLP & 0.4873650 & 0.1104769 & 0.298921 \\ SVM & 0.5798631 & 0.2546394 & 0.417251 \\ DT & 0.7506439 & 0.4934867 & 0.622065 \\ RF & 0.6007535 & 0.3010182 & 0.450886 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Estimated loss functions for used models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Assessment Score**} & \multirow{2}{*}{**Accuracy**} \\ \cline{3-4} \cline{5-6}  & & **Precision** & & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{MLP} & Training & 0.90 & 0.90 & 0.91 & \\  & Testing & 0.91 & 0.85 & 0.91 & 0.901 \\ \cline{1-1}  & Training & 0.85 & 0.85 & 0.87 & \\ \cline{1-1}  & Testing & 0.85 & 0.87 & 0.87 & \\ \cline{1-1}  & Training & 0.81 & 0.83 & 0.83 & \\ \cline{1-1}  & Testing & 0.80 & 0.80 & 0.80 & \\ \cline{1-1}  & Training & 0.85 & 0.83 & 0.83 & \\ \cline{1-1}  & Testing & 0.83 & 0.83 & 0.85 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The evaluation criteria estimate for predictive models. On the other hand, using machine learning algorithms has some limitations that should always be considered. The most important limit of machine learning algorithms is the database. A more extensive database is better and increases the learning rate. In geotechnical engineering, the input parameters are usually broad, with many uncertainties, so the development of highly accurate models must always have sufficient coverage in its database (both in the training and testing sets). The other limitation usually faced in the implementation process of predictive models is related to hardware and system analysis power. The higher system power is responsible for higher accuracy, higher complexity, and speed of analysis. Nevertheless, for future scientific research that might be conducted using machine learning applications by various researchers, it is recommended that scholars give specific attention to these limitations when they want to utilize machine learning methods.