## 1 Introduction  
The mountainous area of Korea covers approximately 70% of the total land. Areas with landslide susceptibility in Korea have been reported in the steep slopes of mountainous areas consisting of granite or gneiss [1]. These conditions, in addition to low strengths of weathered soil and unstable slopes, are considered vulnerable to particularly shallow landslides when intense rainfall occurs during the summer rainy season. There is a tendency that suggests that the risk of landslides is increasing due to frequent localized heavy rain in Korea resulting from recent climate change [1; 2]. In addition, earlier landslides that developed in the upper mountainous areas extend to debris flows in the valley area, and affect property damage and loss of human life in living areas which are developing and expanding [3]. Therefore, landslides are viewed as hazards for human life and artificial structures in Korea. The damage caused by landslides is the same worldwide. To minimize the damage to people and property due to landslides, many efforts over the past few decades have been made to understand how to control landslides and predict their spatial and temporal distribution [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Most approaches have been applied on the Geographic Information System (GIS)-based landslide susceptibility assessment representing predicted landslide risks. The classification of these approaches (e.g., heuristic, statistical, probability, and deterministic approaches) are well documented in [PERSON] et al., 2006. In particular, statistical and probability application models have been widely applied by several studies to predict landslide susceptibility using a past landslide inventory and their environmental factors. The models include frequency ratio [14; 15], weight of evidence [16], logistic regression [17], and fuzzy logic [18]. Recently, data mining techniques have been developed and are extremely popular [19; 20] when dealing with a variety of nonlinear issues. Techniques applied in landslide susceptibility modeling include: artificial neural network, decision tree, boosted tree, neuro fuzzy, Bayesian network, support vector machine, and random forest [21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. When using these approaches to predict landslide-susceptible areas, it is assumed that past landslide occurrence conditions are similar to the conditions for future landslide occurrence [12]. Therefore, it is necessary to train and explore the relationship between past landslide locations and environmental factors (e.g., topographic, hydrologic, soil, and forest data) when using these approaches to predict landslide-vulnerable areas. To do so, it is important to prepare accurate landslide maps and to select environmental variables that affect landslide occurrence to apply to models [31]. Although several different models have been compared in previous studies [22; 26], this study analyzed landslide susceptibility based on artificial neural networks (ANN) and boosted tree (BT) models that have not been applied simultaneously in other studies. Furthermore, as various topographic and hydrologic factors have been calculated from a digital elevation model (DEM) using a System for Automated Geoscientific Analyses (SAGA) GIS Module, and landslide occurrences have accurately been detected from digital aerial photos, the contribution of these factors were evaluated from these models. Therefore, this research aimed to: (i) investigate and compare the performance of data mining-based ANN and BT models, (ii) prepare accurate landslide maps using digital aerial photographs with high resolution, and (iii) determine the contribution of the environmental factors. The preparation of the landslide susceptibility model was accomplished in three major steps. 1. Compilation of a spatial database. A total of 82 debris flows were detected by visual interpretation of aerial photographs with a 50 cm resolution before and after landslide events.