## 4 Results
### Landslides Susceptibility Assessment  
In this article, PyCharm was selected as a compiler to code the deep learning algorithm through Python. After previous experience and trial-and-error [29; 78], the parameters of DNN and SSL-DNN models were determined, as shown in Table 3. Moreover, Figure 9 reflects the variation of accuracy and loss as the iteration progresses. Note that after the number of epochs reached 200, the difference between the training set and the test set in accuracy was less than 0.02, and the loss also tended to be stable, so the networks can be determined to avoid over-fitting.  
Figure 8: The output results of the Pearson correlation matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ParametersMethod** & **DNN** & **SSL-DNN** \\ \hline Epochs & 300 & 300 \\ Dropout & 0.5 & 0.5 \\ Learning rate & 0.001 & 0.001 \\ Number of hidden layers & 3 & 3 \\ Dense connection & 16 & 64 \\ Activation function & ReLU & ReLU \\ Optimizer & Adam & Adam \\ Loss function & Binary cross-entropy & Binary cross-entropy \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings of DNN and SSL-DNN.  
After establishing the above semi-supervised and the comparative models, the landslide susceptibility of all pixels in the study area were predicted. These probability raster maps were crucial to visualize the overall quality of LSM Then, the susceptibility index was divided into five grades by the natural fracture method (Figure 10). The higher the hazard grade, the more concentrated the debris flow distribution and the greater the probability of debris flow disaster. The maps consistently indicate that the southeentral and northwestern parts of the study area are hilly and farmland, with landslides most likely to occur. However, for primeval forest with low human activity, the susceptibility of landslide is very low.  
Figure 11 shows the relative distribution of the landslide susceptibility classes and the landslide density. The results of susceptibility level showed a similar trend (Figure 11a). In the map produced using the DNN model, the proportion of very high susceptibility was the lowest, accounting for 8.61%, and the other 68.26, 4.40%, 8.12%, and 10.61% were very low, low, moderate, and high probability, respectively. The proportion of each susceptibility level in the LSM predicted by the SSL-DNN model as very low (62.62%), low (4.43%), moderate (4.03%), high (13.55%), and very high (15.37%). Regarding the two shallow learning models, of the results the SVM model generated, about 61.32%, 10.19%, 6.84%, 6.59%, and 15.06% were very low, low, moderate, high, and very high levels, respectively. Finally, based on the LR model, approximately 58.29% of the land area was in the very low susceptibility zone and the other 10.15%, 6.75%, 6.96%, and 17.89% were low, moderate, high and very high probability, respectively. At the same time, the landslide density of the above models was positively correlated with the type of susceptibility, as shown in Figure 11b. In the very high susceptibility zone, the SSL-DNN model had the highest landslide density of 22.21 landslides per 100 km\({}^{2}\), while the LR model had the lowest value of 11.15 per 100 km\({}^{2}\).  
Figure 9: The convergence curves. (**a**) Accuracy curves of DNN; (**b**) Accuracy curves of SSL-DNN; (**c**) Loss curves of DNN; (**d**) Loss curves of SSL-DNN.  
Figure 11: _Cont_.  
Figure 10: Landslide susceptibility maps. (**a**) DNN, (**b**) SSL-DNN, (**c**) SVM, (**d**) LR.