# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 4 Results and Discussion
### Evaluation Results  
The modeling process using the training dataset defined the relationship between landslide influencing variables with the location of the past landslides and future landslides, and the results were evaluated through AP scores with PR curves and statistical indicators of RMSE. The AP score curve showed that the performance of the DNN, kernel-based DNN, and CNN models was 99.45%, 99.44%, and 99.41%, respectively (Figure 7d-f). All showed over 99%, indicating that each model has very good accuracy in predicting landslides. RMSE was calculated based on the test dataset for the predictive model (Figure 7a-c). There were highest (0.1806) and lowest (0.1694) RMSEs for the DNN and kernel-based DNN models, respectively, and the CNN model had an RMSE of 0.1747. A low RMSE indicates good predictive power of the developed model. All three models show similar values, but the kernel-based DNN and CNN models were slightly better than the DNN model. This is obviously because the models are based on the kernel approach. However, the benefits of kernel processing were not significant in this study, because (1) the spatial resolutions of the forest and soil maps are low, and (2) the topographic relationships between adjacent pixels are given by the input data, such as TWI, SPI, SLF, etc. We can find one more difference between the DNN and the kernel-based models in Figure 6a-c. The kernel-based models shown in Figure 6b,c are smoothed due to the kernel calculation.  
Figure 7: Model performance evaluation results: (**a–c**) root mean square errors (RMSEs) and (**d–f**) average precision (AP) scores of the DNN, kernel-based DNN, and CNN models.  
The AP scores were almost identical in all models, indicating that the model used in this study was reasonably accurate in mapping the susceptibility of landslides in the study area. The DNN showed the highest accuracy among three models, but the difference compared to the kernel-based DNN was very small, and the RMSE was relatively high. As a result of using an additional kernel, the RMSE value decreased by about 6.2%, and such inconsistency did not occur. Therefore, it can be interpreted that the kernel-based DNN model exhibits better performance for predicting future landslides than the DNN model. In addition, the weight decay was increased to adjust the total weight, in order to prevent the weight of a specific node from being higher than necessary, thereby preventing the result of being weighted by a specific variable. By preventing overestimation, it was possible to prevent the results from being greatly affected by the slightly adjusted training data. Through this, the results of improving the precision of the test data, as well as the precision of the training data, were confirmed. The three results compared in this study all showed similar patterns due to the limited input neurons, and it is difficult to specify which model is better. However, reliable results were obtained in all of the DNN, kernel-based DNN, and CNN models, and can be applied not only to landslides, but also to modeling the susceptibility of natural phenomena and various regions, such as flood and forest fire mapping.