## 2 Materials and Methods
### Dataset  
The genesis of landslide disasters is influenced by a multitude of factors, encompassing the slope's terrain and topography, regional geological settings, hydrogeological conditions, climatic environment, and anthropogenic engineering activities, among others. However, the specific contribution of these factors varies with the nature of the landslide event. When examining the causative agents of landslides, it is imperative to tailor the analysis to the contextual particularities of the study area and to judiciously select the most pertinent factors. The judicious choice of landslide-triggering factors is pivotal to the accuracy and reliability of susceptibility assessments [17]. In this study, a comprehensive set of variables was extracted, encompassing geological strata, fault-influence distance, slope gradient, aspect, elevation, terrain ruggedness, planar curvature, profile curvature, mean curvature, relative elevation, surface roughness, land use categorization, impact distance of water systems, cumulative catchment volume, NDVI, and more, in five distinct dimensions: regional geology, terrain and geomorphology, hydrogeology, climate and environment, and human engineering activities, totaling 17 factors, including annual rainfall and the impact distance of roads. The relevant parameters for the aforementioned datasets are detailed in Table 1.  
Figure 1: Geographic location and satellite images of the study area. Figure 2: Distribution of landslide hazards in the study area. to the standard definition of mutual information. In landslide susceptibility assessment, it signifies how the probability of a landslide is altered when accounting for various contributing factors. Given the complexity of Equation (1) for computational purposes, the equation has been reformulated and simplified as follows:  
\[I(y,x_{1}x_{2}\cdots x_{n})=I(y,x_{1})+I_{X1}(y,x_{2})+\cdots+I_{X1X2\cdots Xn-1}( y,x_{n}) \tag{2}\]  
Equation (2) breaks down the overall information content related to landslide occurrence into a sum of individual information contents for each independent factor (\(x_{i}\)). This suggests that the impact of each factor on the likelihood of a landslide is independent and can be calculated in isolation. However, this assumption may not always be valid in practice due to potential interdependencies among different factors. In landslide susceptibility assessment, the area ratio is more favorable for calculation, so the formula is rewritten in the form of area ratio for calculation:  
\[IV_{i}=\ln\frac{L_{i}/L}{A_{i}/A} \tag{3}\]  
In the context of landslide susceptibility assessment, the utilization of area ratios is often more computationally convenient. Therefore, the formula has been restructured to facilitate calculations using area ratio metrics:  
\[IV=\sum_{i=1}^{n}IV=\sum_{i=1}^{n}\ln\frac{L_{i}/L}{A_{i}/A} \tag{4}\]  
where \(L_{i}\) represents probability and \(A_{i}\) represents area. Equation (4) represents the aggregate of all individual factor information quantities (_IVi_), serving to compute the comprehensive information content for the entire study area's landslide susceptibility. In the evaluation of landslide susceptibility, this composite information content can be utilized to gauge the overall risk of landslides in the area. SVM is a sophisticated machine learning technique that is rooted in statistical learning theory [27; 28]. In this study, we employ the radial basis function kernel to minimize the induction error, thereby applying it for the evaluation of slope susceptibility [29]. The radial basis function kernel serves as a critical component in the SVM model, enhancing its predictive accuracy:  
\[K(_{x_{i},x_{j}})=\exp\biggl{\{}-\frac{\|x-x_{i}\|}{2\sigma^{2}}\biggr{\}} \tag{5}\]  
where \(\|x-x_{i}\|\) represents the squared Euclidean distance between two eigenvectors. Here, \(\sigma\) denotes an adjustable parameter within the model. The introduction of the SVM approach is a synthesis of previous research insights to form a hybrid model.