## 3 Results
### Model Training and Evaluation  
The model was trained using the Adaptive Moment Estimation (Adam) algorithm with a cosine annealing learning rate schedule. The initial learning rate was set to 0.015, with a period of 50 epochs, a minimum learning rate of 0.0005, and a weight decay of 0.0005. A total of 500 epochs were conducted. The training data, comprising 4225 cleaned samples, was split into a training set and a test set at a ratio of 80:20. A weighted cross-entropy loss function was adopted. Four metrics derived from the confusion matrix were employed to evaluate the model performance, including accuracy, precision, recall, and the receiver operating characteristic (ROC) curve along with the area under the curve (AUC). The confusion matrix visually represented the agreement between the predicted results and the true classes by categorizing samples into four groups: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Figure 4: Conceptual relationship diagram of the loess landslide ontology. Figure 5: An example of the constructed knowledge graph. Accuracy refers to the proportion of correctly predicted samples among all instances, where a value closer to 1 indicates superior predictive performance. Precision, also known as exactness, is calculated as the ratio of true positive samples to all samples predicted as positive. Recall, which is alternatively termed sensitivity or comprehensiveness, represents the proportion of true positive samples correctly identified from the total actual positive instances. The receiver operating characteristic (ROC) curve plots the false positive rate (FPR) on the x-axis against recall on the y-axis. The area under the ROC curve (AUC) quantifies the overall performance, typically ranging from 0.5 to 1. A larger AUC indicates that the ROC curve is closer to the top-left corner, reflecting stronger discriminative ability of the model. Specifically, an AUC exceeding 0.9 suggests highly accurate predictions, whereas an AUC below 0.5 implies insufficient predictive capability, rendering the model unsuitable for practical applications. Figure 6 illustrated the variation in the model's loss value with respect to the number of training epochs. The loss value decreased rapidly during the initial phase of training, dropping from approximately 1.2 to below 0.2, which indicated substantial learning capability at the early stage. As training progressed, the loss value gradually stabilized and converged to nearly zero after around 200 epochs, suggesting that the model effectively fit the training data and that further training yielded minimal additional improvement. This behavior demonstrated that the overall training process converged well and that the model exhibited strong learning capacity and stability. Figure 7 presents the confusion matrix for landslide stability prediction, which was used to evaluate the model's classification performance across different stability categories. This confusion matrix was subsequently employed to compute the recall and precision metrics. The final trained model achieved excellent overall performance, attaining an accuracy of 92.66%. As summarized in Table 4, the model performed robustly across all categories, particularly in identifying the Unstable and Relatively Stable classes, where both precision and recall values were relatively high. This indicated strong predictive capability for these two categories. In contrast, while high recall was achieved in the Stable category, precision was comparatively lower, suggesting some degree of misclassification in identify  
Figure 6: Error variation during training. ing stable samples and highlighting an area for further improvement. Overall, the model demonstrated excellent classification performance and effectively discriminated between different stability categories, especially in distinguishing unstable from stable conditions. The AUC curves for different categories were plotted to further evaluate the model's predictive performance across each stability level.