## 1 Introduction  
A landslide is defined as a natural disaster that occurs when gravity causes a mass of debris, soil, or rock to move on a downward slope [1]. The majority of landslides occur as a result of hydroclimatic events, such as prolonged or intensive rain. Furthermore, mechanisms such as seismic triggers, wind, and freeze-thaw cycles are known to initiate landslides [2]. Mountains with shallow layers of soil that have formed in place from weathered gneiss and granite make up roughly 70% of the Korean peninsula [3]. Such terrain is vulnerable to weakening during heavy rainfall. Most of the annual precipitation occurs during the summer, when heavy rain and typhoons frequently occur. In particular, the heavy rain associated with typhoons has the potential to cause landslides in South Korea [4]. The year 2011 was a particularly devastating year, with 43 landslide-related casualties in Chuncheon and at Woomyeon Mountain in the area surrounding Seoul City. This is the largest number of landslide-related casualties since 2000. South Korea has not been alone in experiencing an increase in such natural disasters. Other regions around the world have also experienced more frequent landslides on a larger scale and with more severe damage. In future decades, this trend will probably continue because of ongoing deforestation, increased urbanization, and an increase in regional precipitation in landslide-prone areas due to climate change [5]. It is essential that both susceptible and stable areas be identified to mitigate property damage, environmental degradation, and loss of life. Consequently, landslide susceptibility assessments, i.e., assessments of the spatial probability of a landslide occurring, are a huge step forward in the comprehensive hazard management of landslides [6; 7]. The landslide susceptibility map (LSM) produced by a landslide susceptibility assessment can be a useful tool for authorities with decision-making capabilities. Many methods and techniques have been proposed to evaluate landslide susceptibility. In the past few decades, statistical approaches have become popular in the use of remote sensing (RS) with a geographic information system (GIS). There are many statistical approaches used in landslide susceptibility assessment, including a frequency ratio (FR) [8; 9], certainty factor (CF) [10], statistical index (SI) [11; 12], as well as weight of evidence (WoE) [7; 13; 14] and logistic regression (LR) [15; 16] approaches. Recently, machine learning techniques have become popular in various fields. Machine learning, a branch of artificial intelligence, uses computer algorithms to analyze and predict information based on learning from training data [17; 18]. Due to its robustness and high generalization capability, the use of machine learning has increased in landslide susceptibility analysis. Among the machine learning methods, artificial neural network [19; 20], fuzzy logic [21; 22], neuro-fuzzy [23], support vector machine [24; 25], random forest [26; 27], and naive Bayes tree [17; 28] methods have been popularly applied. More recently, ensemble machine learning techniques have been used to enhance the prediction power and robustness of landslide susceptibility assessment. The ensemble methods, formed by a combination of variously based classifiers, have typically demonstrated significant improvement [17; 24; 29; 30]. Ensemble techniques, which are relatively new approaches for producing a landslide susceptibility map, have been rarely used in the field. Therefore, the main objective of this research was to analyze and compare the performance of different ensemble models--namely, the random forest (RF) and boosted regression tree (BRT) models--for landslide susceptibility analysis. The RF and BRT models are very popular ensemble methods. Both are tree-based algorithms that predict the results by combining individual trees. However, the RF and BRT models build trees in different ways. Considering these characteristics, these models are appropriate for producing LSMs and for comparing LSM results.