The soil depth of mountainous areas of Korea is shallow; the rainfall is concentrated in a certain season. Although the number of days of rainfall has decreased compared to the past due to the effects of abnormal weather conditions, the frequency of occurrence of landslides caused by localized heavy rain has increased and the amount of damage has increased.
The damage to human and physical resources caused by landslides is occurring every year, which is increasing the social interest in this matter.
In order to prevent such landslides, it is necessary to have a highly accurate forecasting study on landslide hazard ares through systematic researches on various factors such as topography, geology, rainfall, and forest physiognomy.
In this study, we examined the factors affecting landslide occurrences and developed LSMAP, a mountainous landslide prediction program that takes parameters such as root cohesion, tree weight, ground characteristics, soil depth, and rainfall scenarios.
LSMAP reflected local conditions by taking into account not only the geologic, terrain, and rainfall factors considered as parameters in the existing landslide hazard area prediction model, but also the forest physiognomy characteristics.
Among the preliminary intensively-managed areas for sediment disasters in urban area chosen by the KRIHS(Korea Research Institute for Human Settlements), we selected 10 areas as research sites and used LSMAP to analyze landslide hazard area.
Field survey and literature search were conducted to establish the key factors applied to the analysis, and samples were taken from the study area and soil tests were conducted.
Moreover, we conducted a comparative analysis of LHMAP (Landslide Hazard Map by Korea Forest Service), SINMAP, and TRIGRS, which are commonly used for predicting landslides, and verified the applicability of LSMAP by comparing actual landslide occurrence points with prediction results.
As a result of the review, SINMAP, TRIGRS, and LSMAP were considered to show similar patterns in accordance with the analytical methods applied to each model.
In addition, SINMAP, TRIGRS, and LSMAP relatively predicted the actual landslide occurrence points in comparison with actual landslide occurrence history.
However in the case of SINMAP, it was somewhat difficult to conclude that it precisely predicted the actual landslide occurrence points since it predicted a wide area in the basin as a landslide hazard area.
the above results were based on limited study area and test volume. in the future it will be possible to make a more reasonable predictions by analyzing the correlation between the parameters, which are to be applied through review of more case further study and testing, and applying the results to the LSMAP.


Keyword : Landslide, Prediction of hazard areas, LSMAP