## 5 Discussion and Conclusions  
Digital aerial photographs of high resolution are very useful in constructing detailed landslide inventory maps, as it is difficult to separate the similar shapes of landslide scar areas and surrounding tombs in the study area using satellite images or panchromatic aerial photographs. Therefore, both shapes could be easily interpreted visually in high-resolution aerial photographs taken in a high-vegetation season. Using aerial photographs could also save time and costs in field surveying to identify damage from natural disasters. In Korea, debris flows occur randomly in several slope regions due to intensive rainfall per day. Therefore, it is necessary to select factors related to landslide occurrence and analyze the landslide susceptibility using pattern classification by looking at the relationship between the various factors and landslide location. However, it is not possible to know quantitatively how the environmental factors relate to the occurrence of landslides. ANN and BT models, which are used in many fields as sophisticated modeling techniques, were applied in identifying the influence of environmental factors to landslide occurrence and in mapping landslide susceptibility. The training and validation sets, which were used in the ANN and BT models, were the same: 50% and 50% of a total of 82 landslide occurrences, respectively. ANN modeling was performed while changing the number of hidden layers (18 to 36), the value of learning rate (0.1 to 0.01), and RMSE (0.01-0.001). A sigmoid function for a backpropagation network was used as one of the more popular activation functions. The result of ANN modeling was best in 36 hidden layers, with 0.01 of learning rate and 0.001 of RMSE. BT modeling was performed at 0.01 of learning rate, 5 of tree complexity, and 0.5 of bag fraction. The optimal number of trees was reached at 262. The weights of all factors from the ANN and BT models were normalized from 0 to 1. The factors' weights were divided into three groups: high, medium, and low influence groups from the ANN and BT models. The high group of ANN and BT included three factors: timber age, TWI, and slope gradient. The medium group of both models had three factors: TPI, soil land-use, and SPI. The low group had four factors: MSP, soil topography, plan curvature, and soil thickness. Although it was difficult to identify the influence ranking of the environmental factor (i.e., topographic, hydrologic, soil, forest, etc.) to landslide occurrence because of these intersections from intensive rainfall, the common  
Figure 6: Percentage of area under curves (AUCs) of the landslide susceptibility maps based on ANN and BT models. factors in each ANN and BT group could be identified and can be used as a guideline for selecting environmental factors affecting landslide occurrence in other study areas in Korea. ANNs are capable of handling complex and robustly nonlinear processes without previously assuming the relationships between the input and output variables ([PERSON] et al., 2014). Boosted tree has all of the strengths of decision trees, including the advantage of being able to handle both continuous and categorical variables ([PERSON] et al., 2017). In this study, the validation result of the ANN and BT models was 82.25% and 90.79%, respectively, which demonstrated reasonably good performance. In particular, the BT model had a higher accuracy (about 8%) than the ANN model. In other fields (using both models) ([PERSON] et al., 2017; [PERSON] et al., 2017), BT reported a better performance than ANN. The results of this study demonstrate the benefits of selecting optimal data mining techniques in landslide susceptibility modeling. For future study, it is necessary to generalize the regularized relationships between the classes of each factor and landslide occurrence, and to combine the influence score of factors and factor classes. This approach could be used as a guideline to apply threshold values to a landslide monitoring system in the Korean rainy season.