# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 1 Introduction  
Natural disasters are becoming more frequent around the world due to extreme climates, and an increase in the probability of extreme rain is also associated with the occurrence of damage. In Korea, the current rainfall and rainfall intensity have gradually increased compared to the past, and the rainfall is concentrated in a small area over a short time. A lot of water-related disaster damage has occurred in this localized heavy rain, but the damage is also decreasing due to continuous river maintenance and the installation of facilities to prevent flooding. On the other hand, the number of casualties,134 people, caused by landslides and slope failures has not decreased since the 2000s [1]. Recently, the incidence of localized rainfall increased 2.4 times, from an average of 5.1 in the 1970s to 12.3 in the 2000s [2]. Due to the increase in localized heavy rain, landslides and geomorphological hazards, such as debris flows, are rapidly increasing [3]. Landslides in Korea occur intensively in June to September, when heavy rains and typhoons occur, costing many lives and significant property damage. The area of landslide damage since the 2000s has been more than three times that of the 1980s [4]. In particular, serious damage to human lives has occurred in the years of torrential rains and typhoons, which is a serious social issue [5]. This kind of phenomenon is occurring not only in Korea, but also in other parts of the world, raising concerns about the occurrence of landslides. Damage increases due to a combination of natural and artificial causes; the increase in localized heavy rain is an example of a natural cause, and an indiscriminate development is a representative artificial cause. Development projects in mountainous regions have increased due to the tendency to prefer a nature-friendly environment. The risk of landslides is increasing due to the increasing number of cases where residential facilities, such as apartments, are located near steep slopes. When torrential rainfall occurs, the soil begins to be saturated, the pore pressure increases, and the shear strength decreases. In addition, when rainfall infiltrates the bedrock, the adhesive strength weakens. The possibility of landslides increases when the soil is not able to support the increased weight of the saturated soil. Artificial steep slopes have the same characteristics; the expected damage range is a horizontal distance equal to twice the height of the slope. The steep slope in an urban area needs essential management for reducing the risk from landslides. In particular, debris flow is a phenomenon in which debris, such as rock fragments, soil, and mud move in groups with high speed. The debris, clay and rocks increase its volume and flow with strong erosion power, so that debris flow is the most dangerous type of landslide; research on debris flow is being conducted in various aspects [6; 7; 8]. It could lead to a large-scale disaster, with a loss of roads and damage to houses and people, especially when it occurs in urban areas. In order to prevent or minimize damage from debris flow, it is important to install disaster prevention facilities by predicting movement paths and sedimentary areas before the event [9]. A debris barrier is a fundamental way to protect people and property from debris flow, but it is practically impossible to install it in all dangerous paths. Also, there are the difficulties related to modeling debris flows, due to the uncertainties in defining debris flow rheology behavior, such as the transition from a quasi-static regime to liquid regime [10]. The effects of grain concentration could lead to a dilatant or pseudoplastic fluid behavior, depending on grain volume concentration [11]; finally, the presence of coarse sediment fraction could affects both yield stress and the transition between solid-like to fluid-like behavior [12]. Therefore, it is more efficient to install facilities by predicting the extent of damage to the debris flow [13; 14].