## 3 Dataset Description and Processing  
### Dataset Preparation  
Geological materials such as soil, rock, and sand are exposed to a complex environment of strain rates, water, and temperature changes over a long period time, and their complex pore structures result in extremely discrete mechanical properties [57; 58; 59; 60; 61]. Due to the limitations of open available geological data, it is not possible to comprehensively consider all potential variables. This section compiles a dataset encompassing 307 sets of data based on available measurements reported in the literature [62; 63; 64; 65; 66; 67] for training and developing ML models to predict FoS. Referring to the practice of ref. [68], the six most representative variables were selected to evaluate FoS. These six key variables include friction angle (\(\varphi\)), cohesion (\(c\)), unit weight (\(\gamma\)), slope height (\(H\)), slope angle (\(\Phi\)), and pore pressure ratio (\(r_{u}\)). Here, \(\varphi\), \(c\), and \(\gamma\) reflect the mechanical properties of the rock and soil mass, \(r_{u}\) reflects the magnitude of the pore water pressure, and \(H\) and \(\Phi\) reflect the geometric characteristics of the slope. The specific definitions of these six key variables are as follows:  
* \(H\) indicates the vertical distance between the slope crest to slope base. * \(\Phi\) represents the angle between the inclined slope plane and slope base. * \(r_{u}\) denotes the ratio of pore water pressure to overburden pressure and represents the external triggering factor of landslide. * \(\gamma\) indicates the weight per unit volume of geomaterials. * \(\varphi\) and \(c\) represent the geomaterial's ability to withstand shear stress. Descriptive statistics are an important part of data analysis and provide the basis for subsequent ML modeling. Table 1 provides univariate descriptive statistics of the data to summarize the data in an organized manner. The input variables collected in different slopes have certain differences. Visual analysis can reflect the characteristic parameter information in the dataset and can qualitatively analyze the slope state to a certain extent. In order to evaluate whether there are outliers in the dataset, we plotted the violin plot of each variable in the dataset, as shown in Figure 2. In general, violins combine the box plots and the kernel density plots to effectively depict the data distribution. The shape of the violin indicates how the data are distributed along the vertical axis, and the interior of the violin contains a box plot. Figure 2 shows that most of the cohesion is distributed in 10-36 kPa; the slope angle is mainly 25\({}^{\circ}\) to 45\({}^{\circ}\). The slope height is mainly 40.3 m. A cursory inspection of Figure 2 indicates a relatively uniform distribution of the input variables in this dataset. There are no outliers or anomalous data in the violin plot, indicating that this collected dataset is constructed reasonably. To study the correlation between different input variables, Figure 3 analyzes the correlation between the six key input variables of the dataset and the output results FoS. It can be seen that the correlation coefficient of two identical variables is represented diagonally from bottom left to top right as the symbol of the variable (representing a correlation coefficient of 1). The correlation coefficient of the part above the diagonal is the same as that of the other part of the symmetry. The correlation coefficients between the different input variables and FoS are relatively low (most values are less than 0.4). That is