## 4 Analysis of Evaluation Results
### Results of the Vulnerability Evaluation
The accuracy of SVM, on the other hand, depends entirely on the chosen kernel function, and these variables contain linear function, radial basis function (RBF), sigmoid, and polynomial. Due to the better stability and robustness of the RBF radial basis function, the RBF radial basis function is chosen as the kernel function of the prediction model in this study. Parameter selection is the most critical in the support vector machine model, and the parameter selection is directly related to the prediction performance of the model. In this study, an intelligent optimization search was conducted by PSO, which can find the potentially optimal particle swarm through each particle movement iteration and the overall optimal position by using the continuous updating of the particles and determining the direction of its movement and speed. Furthermore, it can balance the global and local searches of the PSO algorithm, introduce inertia weight, and finally obtain the model optimization results. As shown in Table 4, when initializing the particle swarm optimization calculation, the number of particle swarms in it is adjusted to 50, and the highest evolution frequency in it is adjusted to 200. At the same time, the local search ability factor c1 is adjusted to 1.3, the global search ability factor c2 is adjusted to 1.5, and the inertia weight \(\omega\) is adjusted to 0.6. Furthermore, the initial coefficient wV is adjusted to 1, and the initial coefficient wP is adjusted to 1. After the iteration, the SVM parameter optimization results are obtained as follows: penalty factor C = 1 and kernel parameter gamma = 0.02. Substituting the parameters into the model operation, the landslide susceptibility results are obtained. In this study, all the landslide evaluation factors are combined together, and each pixel can be regarded as a one-dimensional feature vector. These vectors together constitute a one-dimensional array of the study area. Using the sample data, a ratio of 7:3 was randomly selected to form the training set and test set for model training and construct a one-dimensional convolutional neural network based on keras. Firstly, the one-dimensional sample data consisting of 13 evaluation factors were input into the model. Then, a size 3 convolutional layer was constructed, and 32, 64, and 128 kernel 3 filters were selected for feature extraction, respectively. An average size 2 pooling layer was then \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Note** & **Parameters** & **Note** \\ \hline kernel & RBF & c1 & 1.3 \\ C & 1 & c2 & 1.5 \\ gamma & 0.02 & \(\omega\) & 0.6 \\ number of PSO & 50 & wV & 1 \\ frequency & 200 & wP & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSO-SVM parameters. defined behind each convolutional layer while keeping the output dimension unchanged to extract the salient features and at the same time reduce the parameters. In accordance with the work of [PERSON], [PERSON], and [PERSON], who used the tanh activation function for comparison experiments, after comparison, we selected the tanh activation function, using the Bayesian optimization algorithm to adjust the model hyper-parameters to optimize the performance of the model, obtaining batch size = 128 and epoch = 200, which were the best results. The result of landslide susceptibility was then obtained by substituting the parameters. To determine the processing and visualization of the landslide susceptibility prediction results, ArcGIS10.2 software combined with the resultant data obtained from the model was used. The predicted values of the dataset were then converted into raster image elements through the point-to-raster in the conversion tool of the software. Following this, the landslide susceptibility evaluation value of the whole study area was graded by using the natural segment point method of the software and divided into five susceptibility grade intervals: low susceptibility zone, lower susceptibility zone, medium susceptibility zone, higher susceptibility zone, and high susceptibility zone.