## 3 Methodology
### Model Construction and Evaluation  
#### 3.3.1 Construction of the Dynamic Landslide Susceptibility Evaluation Model  
The SVM constitutes a significant supervised learning approach for evaluating hazard susceptibility. It aims to identify the optimal decision boundary to distinguish positive and negative samples within a training dataset [54; 55]. SVMs are frequently employed in LSM owing to their accuracy, robustness, and generalization ability [56], making them the comparison model for this study. CNNs are among the most popular DL algorithms and have attracted attention for their superior performance in image vision [50; 57]. Th CNN is mainly composed of convolution, pooling, activation, and a fully connected layer (Figure 4a). The convolutional layer plays a crucial role in capturing spatial hierarchies and identifying complex relationshipsbetween different features within the input data, thereby enhancing the network's ability to extract meaningful representations. The output of the convolutional layer is subsequently processed by the activation layer, where the ReLU (rectified linear unit) activation function is commonly employed. The ReLU effectively introduces nonlinearity into the model, significantly improving its capacity for complex pattern recognition. The pooling layer follows, performing downsampling to reduce the dimensionality of the feature maps, thereby mitigating the risk of overfitting and improving model generalization. Finally, the fully connected layer integrates the pooled features to produce the final output. CNNs are extensively applied in landslide susceptibility modeling due to their robust feature extraction capabilities, ability to learn spatial patterns, and parameter-sharing mechanism, which significantly reduces the computational complexity of large-scale datasets. ResNet is a sophisticated deep CNN designed to address gradient vanishing and network degradation during training. This was proposed by [PERSON] [13] at Microsoft Research in 2015; its fundamental unit is the'residual block'. Each block includes multiple cascaded convolutional layers, activation functions, and batch normalization. The output encompasses two distinct components: One is an unprocessed direct result derived from input data \(x\), known as shortcut or skip connection, and the other component represents \(F(x)\) added together as \(x\), resulting in \(x+F(x)\). This process facilitates effective information transfer across various network layers, mitigating vanishing gradients. ResNet involves the repetitive stacking of these residue units, leading to diverse types such as ResNet18, ResNet34, and ResNet50. This study utilizes landslide susceptibility data that encompass a diverse array of high spatial resolution datasets, including key indicators such as the NDVI, DEMs (digital elevation models), and other geospatial layers. These datasets exhibit complex spatial patterns that are essential for the precise modeling of landslide susceptibility. The architecture of ResNet18, with its residual connections, is particularly well-suited for capturing these complex spatial dependencies, enabling the model to learn hierarchical features effectively  
Figure 4: The network architecture. (**a**) The CNN network architecture, (**b**) the residual unit, and (**c**) the ResNet18 network architecture. while mitigating the vanishing gradient problem, even when processing high-resolution spatial data. Furthermore, the global scope of the study necessitated the use of a large, diverse dataset with numerous input features derived from heterogeneous data sources. In comparison to deeper neural network architectures, ResNet18's relatively lightweight design offers advantages. It allows for efficient training on large-scale datasets without imposing excessive computational demands. This ensures that the model can be deployed over extensive geographic areas without compromising processing time or memory capacity.