Most of the slope failures and debris flows occur due to seasonal rain and typhoon in Korea. Rainfall is very important factors induced by the landslides. To study the establishment of landslide rainfall thresholds, the data of rainfall and landslides are investigated and analyzed. Landslide rainfall thresholds on landslide assessment are based on an empirical approach. It assumes that an area which experienced landslides with specific rainfall characteristics will experience similar landslides when similar rainfalls occur. If rainfall characteristics in a specific area can be observed in real time, it is possible to occur the landslide. The analyses focused on 50 historical landslide events and rainfall data. These data are analyzed to know the relationship the rainfall intensityduration and landslides. The landslide rainfall thresholds using historical data are very effective method to predict the burst time of landslides.

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Landslide occurs mostly during rainy seasons in Korea, and rainfall is the largest factor causing landslides. This study is intended to establish the rainfall standard to predict the risk of landslides, taking into account of regional / geological characteristics. To that end, historical data of landslides and rainfall data were investigated and analyzed. To predict the landslide risk using rainfall data, rainfall data which were related to causing landslide previously, was selected and analyzed to establish the rainfall standards and real-time rainfall approach was applied to establish the rainfall standards and determine the potential risk. Reliability in predicting the landslide risk using rainfall data, is heavily dependent on the spatial density of rainfall observatory stations and in this study the risk analysis using rainfall data focuses on predicting the time of landslide occurrences. In this study, rainfall standards by region are proposed using 250 landslide historic data and past rainfall data. Reliability of established rainfall standards was verified and rainfall standards would lead to reliable prediction of landslide occurrence time.

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