• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1205-1214
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• 2021

Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (P_daily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to P_daily only threshold, Goesan showed an improvement of 75% (P_daily + API) and 42% (P_daily + SM) and Changsu showed an improvement of 33% (P_daily + API) and 44% (P_daily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.

• The Journal of Engineering Geology
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• v.18 no.2
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• pp.191-196
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• 2008

This study is a case study for application of the RTI warning model to Korea which was previously developed to predict landslide potential and occurrence time during a rainfall event. The rainfall triggering index (RTI) is defined as the product of the rainfall intensity I (mm/hr) and the effective accumulated rainfall $R_t$ (mm). This index is used to evaluate the landslide and debris-flow occurrence potential at time t during a rainfall event. The upper critical value ($RTI_{UC}$) of RTI and the lower critical value ($RTI_{LC}$) of RTI can be determined by historical rainfall data of a certain area. When the rainfall intensity exceeds the upper critical value, there are high potential to occur land-slides. The analysis result can predict landslide occurrence time of an area during a rainfall event as well as land-slide potential. The result can also be used as an important data to issue early-warning of landslides. In order to apply the RTI warning model to Korea this study analyzed rainfall data and landslides data in Inje county, Gangwon province, Korea from July 13 to July 19, 2006. According to the analysis result, the rainfall intensity exceeded the upper critical value 23 hours ago, 11 hours ago, and 9 hours ago from 11:00 in the morning, July 16. Therefore, landslide warnings would be issued three times for people evacuation for avoiding or reducing hurts and dam-ages from landslides in mountainous areas of Inje.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.3
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• pp.19-32
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• 2002

Topographic information is indispensable in the applications that require elevational data. These applications are exemplified by watershed partition, extraction of drainage networks, viewshed analysis, derivation of geomorphologic features, quantification of landslide-terrain, and identification of topographic settings susceptible to landsliding. Therefore, we study the accuracy of data on topographic parameters derived from digital elevation models(DEMs). This research wished to analyze the effect that data source and grid size get in topography parameter using gridded DEM. An analysis of topography parameter extract and compared drainage basin, watershed slope, stream network using DEM is constructed by digital map and DTED DEM. Especially, when extract stream network from gridded DEM, received much effects according to threshold value of flowaccumulation regardless of DEM grid size. Therefore, this study applied equal threshold value of flowaccumulation for two data sources, and compare and analyzed stream network.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.270-283
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• 2024

Purpose: Considering the rising frequency of earthquakes in Korea, it is crucial to revise the rainfall thresholds for landslide triggering following earthquake events. This study was conducted to provide scientific justification and preliminary data for adjusting rainfall thresholds for landslide early warnings after earthquakes through soil physical experiments. Method: The study analyzed the change in soil shear strength by direct shear tests on disturbed and undisturbed samples collected from cut slopes. Also, The study analyzed the soil strength parameters of remolded soil samples subjected to drying and wetting conditions, focusing on the relationship between the degree of saturation after submergence and the strength parameters. Result: Compaction water content variation in direct shear tests showed that higher water content and saturation in disturbed samples led to a significant decrease in cohesion (over 50%) and a reduction in shear resistance angle (1~2°). Additionally, during the ring shear tests, the shear strength was observed to gradually decrease once water was supplied to the shear plane. The maximum shear strength decreased by approximately 65-75%, while the residual shear strength decreased by approximately 53-60%. Conclusion: Seismic activity amplifies landslide risk during subsequent rainfall, necessitating proactive mitigation strategies in earthquake-prone areas. This research is anticipated to provide scientific justification and preliminary data for reducing the rainfall threshold for landslide initiation in earthquake-susceptible regions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.