## 2 Materials and Methods
### DNN Construction Process  
The areas within Moxi Township that had experienced landslides were transformed into grid points. Through a method of trial and error, the optimal training parameters for the neural network model were fine-tuned, and the information for the entire study area was fed into the trained model to generate predictive values. Utilizing a well-trained DNN model, predictions were made across the entire study area, with each grid cell being classified and assigned a susceptibility index, culminating in a total of 822,714 indices. The higher the probability that the model assigns a grid cell to be classified as a \"landslide,\" the closer the susceptibility index is to 1; conversely, the closer it is to 0. The predictive values for all grid data points (822,714) in the study area were exported and, using ArcGIS software (_ArcGIS Pro_), linked to the study area's base map through a data association feature, aligning the predictive values with each grid point in the study area. Thereafter, the \"feature to raster\" function was employed to vectorize the susceptibility indices of all grid cells, producing a landslide susceptibility assessment map. To enhance visual clarity, the natural breaks method was applied to categorize the indices into five tiers: low, lower-medium, medium, higher-medium, and high, subsequently exporting and visualizing the predictive values. The susceptibility zoning map was then divided into five levels: high susceptibility area, higher-medium susceptibility area, medium susceptibility area, lower-medium susceptibility area, and low susceptibility area. Within the low susceptibility area, a number of grid points equivalent to the landslide geological-disaster grid points (787 points) were randomly selected to serve as non-landslide points.  
The rationale behind the construction process was omitted from the article to avoid redundancy. When integrating the factors, they were categorized into five groups, and the number of grids in each group corresponding to different factors, was tallied. Utilizing the information quantity-calculation formula, the information quantity value for each factor was determined. A weighted overlay of all factors yielded the information quantity value for the entire study area. Ultimately, the information quantity values of the nine factors served as input for the SVM model, with the dataset being the one chosen via the DNN method, thus forming the DNN-I-SVM hybrid model, which was then trained. After optimization and parameter selection, the Gaussian Radial Basis Function (RBF) was selected as the kernel function, with the two optimal parameters for the C-SVC type SVM being determined using a five-fold cross-validation approach: the penalty factor C = 10, and the kernel function parameter \(\gamma\) = 0.5. The SVM sample selection mirrored that of the BP neural network, with a total dataset comprising an equal number of positive (landslide points) and negative (non-landslide points) samples, totaling 1574. From this dataset, 70% were randomly chosen for training, and 30% for testing. Upon completion of the training phase, the final DNN-I-SVM model for landslide susceptibility assessment was established, and the results were stratified. To improve visual representation, the natural breaks method was once again used to reclassify the indices into five distinct levels: low, lower-medium, medium, higher-medium, and high.