## 5 Discussion
### Susceptibility in SSL-DNN  
The experimental results of the above models were compared, and the validating outcome (Table 4) indicated that the results of the DNN (0.857) and SVM (0.852) models in supervised learning were obviously better than those of the LR model (0.780). Deep learning, as an artificial neural network with multiple hidden layers for nonlinear transformation of the original data was developed to abstract implicit features. There is no doubt that the application of DNN in LSM was successful. However, from the results of supervised learning, the performance of the DNN model did not seem to reveal much advantage over the SVM model. According to previous studies, the SVM model can maintain the capacities of prediction and generalization when the sample size is insufficient. Furthermore, the DNN model tends to overfit the training data relatively. Even a simple linear model may be superior to deep network models. Predecessors have suggested that efforts should be devoted to find ways to address the problem of insufficient datasets [43; 79]. In this study, the dropout layer can alleviate the above problem to some extent. Furthermore, 3000 unlabeled points were randomly selected to obtain corresponding pseudo-tags for semi-supervised learning. Comparing the results of semi-supervised learning with those of supervised learning, the semi-supervised deep learning achieved the optimal performance (0.898). The purpose of pseudo \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pairwise Model** & \(p\)**-Value** & **z-Value** \\ \hline SSL-DNN vs. DNN & 0.000 & \(-\)5.925 \\ SSL-DNN vs. SVM & 0.000 & \(-\)4.752 \\ SSL-DNN vs. LR & 0.000 & \(-\)4.888 \\ DNN vs. SVM & 0.000 & \(-\)8.146 \\ DNN vs. LR & 0.000 & \(-\)9.097 \\ SVM vs.LR & 0.001 & \(-\)4.616 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Models pairwise comparison using the Wilcoxon signed-rank test. labels in LSM is to obtain the binary classification results of unlabeled points. This avoids the problem that too many predictive categories will introduce large error signals. It seems to be inferred that the regression problem of dichotomies is more suitable for semi-supervised learning applications. For the selection of unlabeled data, while ensuring that the unlabeled information fully reflected the regional characteristics, the extra cost of calculation caused by too much data was avoided as much as possible. In terms of the degree to which the models were optimized, there were significant differences between the results of each model in Table 5. This indicates that the benefit brought about by unlabeled samples and semi-supervised learning was also valuable. The unlabeled data produced an effect like regularization, which reduced the over-fitting of the network under the limited labeled data, and enhanced the ability of generalization. Furthermore, the fitting ability of the classifier would determine the upper limit of semi-supervised learning to some extent. The DNN model was selected as the basic model for semi-supervised training, and the expansion of the sample enabled the network to more fully capture the complexity of landslide distribution. Intuitively, the SSL-DNN model organically integrated the distribution information hidden in the unlabeled sample data. Meanwhile, the multi-layer nonlinear structure enabled it to have strong capability of feature expression and modeling for complex tasks. Although the SSL-DNN model can be confirmed as an alternative with an ideal degree of predictive accuracy, there are still several limitations in the experiment. Due to the lack of theoretical basis, the selection of the super-parameters and network design were also a considerable challenge. Many trial and error tests may be required for a static structure. Second, to facilitate the classification of pseudo labels, the cluster number of K-means algorithm was fixed at 2, which may not obtain the best clustering result.