# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 1 Introduction  
The rapid growth in data due to the development of information and communication technology (ICT) has spurred the demand for data mining, which is generally considered to be the most useful analytical tool in the analysis of large data sets. Data mining has been defined in various ways previously, and is commonly referred to as an exploratory analysis tool for large amounts of data [1]. Therefore, data mining is a process of finding useful information that is not easily exposed in a large amount of data, most recently known as \"big data\". With the rapid growth of ICT since the 1980s, countries and corporations have made significant efforts to build databases to store and manage vast amounts of data. Rational and rapid decision-making is necessary to increase the utilization of large-scale databases. In this environment, it has become important to find meaningful new information that can support optimal decision-making. In this process, an efficient methodology for data mining that extracts not only known information frompreviously established databases but also information that was not previously known has gained popularity. Therefore, the data mining methodology makes it possible to effectively analyze large amounts of data in a database and the relationships between parameters. In recent years, storms bringing localized heavy rains have been reported worldwide, and precipitation in the mid-latitude regions has increased since the 20 th century [2]. In addition, rapid climate change and abnormal climate conditions have led to extreme weather phenomena, which provide the appropriate conditions for the creation of landslides, and the frequency of landslides has increased accordingly [3]. A landslide is the movement of soil down a slope when the stress exceeds the strength of the soil material due to rapid changes in the natural environment such as heavy rains or earthquakes [4]. Landslides are caused by various factors, such as topography, geology, soil characteristics, forest conditions, and climate variables [5; 6; 7; 8]. The main purpose of this study was to generate and validate a landslide susceptibility map using data mining models for Umyeonsan, South Korea. Therefore, data mining techniques should be used appropriately for the analysis of the relationships between landslides and impact factors on landslides in landslide susceptibility analyses. Additionally, given that the data mining models derive their results from the database, the input data represent a key part in the model. Thus, the database should be constructed using reliable data, such as nationally distributed government data. Remote sensing data is one of the main sources to be used to generate the reliable data [9; 10; 11]. Due to the complex factors influencing landslides, it is important to perform an appropriate assessment of the potential for landslide susceptibility using data mining techniques. A susceptibility analysis based on data on previous damage should be performed to define landslide susceptibility in areas where landslides are anticipated so that the damage can be predicted in advance and appropriate mitigation measurements adopted. Over the past several decades, a number of data mining approaches have been developed for mapping landslide susceptibility [12; 13]. The most commonly used methods are soft computing or statistical techniques such as artificial neural network (ANN) models [14; 15], fuzzy logic methods [16; 17] or logistic regression models [18; 19]. Landslides are caused by multiple interactions between various factors, including geometry, geology, soil characteristics, and vegetation conditions [20; 21], and are highly dependent on terrain features [22; 23]. As geological hazards, landslides can limit the sustainable development of urban areas, causing environmental damage, serious casualties, and loss of property [24; 25]. An appropriate assessment of the susceptibility for landslide damage is required to minimize the complex array of losses.