# Comparative Analysis for Slope Stability by Using Machine Learning Methods
## 4 Results and Discussion  
Slope stability analysis evaluates the stability condition of a slope undergoing movement. F.S is a crucial aspect of a numerical description of slope stability. Various methodologies are used to investigate slope stability conditions, which have a background of up to 300 years [6]. With the advancement of technology, these methods have become more com \begin{table}
\begin{tabular}{l|l|l} \hline
**Classifier** & **Hyperparameters** & **Elements** \\ \hline \multirow{3}{*}{MLP} & Hidden layers’ size & Activation = ’relu’; \\  & Learning rate & Optimization = rmsprop; \\  & Optimization & Loss\_function = ’mse’; \\  & & Metrics = ’mae’ \\ \hline SVM & Kernels & Kernel = ’poly’; Degree = 2 \\  & C value & C = 100; Epsilon = 0.1 \\ \hline \multirow{3}{*}{DT} & \multirow{3}{*}{Max depth} & Criterion = ’gini’; Max\_depth = 5 \\  & & Ccp\_alpha = 0.0; \\  & Random state & Min\_samples\_leaf = 1 \\  & & Random\_state = 100 \\ \hline \multirow{3}{*}{RF} & \multirow{3}{*}{Number of estimators} & Criterion = ’entropy’; \\  & & N\_estimators = 10; \\ \cline{1-1}  & Max depth & Max\_depth = 5; \\ \cline{1-1}  & & Min\_samples\_leaf = 1; \\ \cline{1-1}  & & Min\_samples\_split = 2 \\ \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters used in the utilized models. plex and their accuracy has increased. Generally, all applied/introduced methods require several input data which are measured by geotechnical procedures and tests known as geotechnical characteristics of the slope, and output became F.S. using artificial intelligence (machine learning) approaches, which are one of the most recently developed techniques that have received remarkable attention from geo-engineers, due to their capability for accurate evaluations of stability and F.S. Typical machine learning methods are utilized in this article to process and predict the F.S for earth slopes. The predictive models were implemented on actual data to construct the primary dataset. The dataset belongs to 100 stability analyses recorded from the Fars, Isfahan, and Tehran provinces in Iran. The slopes are mainly selected from the road cut projects concerned about stability condition and its effects on roads. Therefore, the study results are susceptible to stabilization methods that imply rectification of the situation. All slopes were investigated and the geotechnical properties of earth slopes were estimated during field surveys and laboratory experiments. Tables 2 and 3 provide information about the analyzed slopes' geometrical and geotechnical properties. To obtain the geotechnical characteristics of the earth slope mass, some specimens were taken and moved to the geotechnical laboratory. The index geotechnical tests, such as uniaxial compression strength, UCS [41], direct-shear [42], and physical properties [43], are directly used in the stability analysis of slopes. Physical properties of the soils can be categorized as water content, specific gravity, and density (\(\gamma_{\text{d}}\)), which has a standard procedure for estimation developed by the American Society for Testing and Materials (ASTM). The density (\(\gamma_{\text{d}}\)) is the fundamental physical properties used in stability assessment as well as soil strength parameters, which include cohesion (c), soil internal friction (\(\varphi\)), Poisson's ratio (\(\
u\)), and stiffness indexes. The stiffness indexes are the soil's deformational modulus, such as young or elastic modulus (E), shear modulus (G), and bulk modulus (k). The stiffness parameters can be calculated by using empirical equations, which are presented as follows [1]:  
\[\text{G}=\text{E}/2\;(1+\
u) \tag{2}\]  
\[\text{K}=\text{E}/3\;(1-2\
u) \tag{3}\]