• Journal of the Korean GEO-environmental Society
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• v.20 no.6
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• pp.15-22
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• 2019

In this study, the hazard areas are identified by using the Newmark displacement model, which is a predictive model for identifying the areas at risk of landslide triggered by earthquakes, based on the results of field survey and laboratory test, and literature data. The Newmark displacement model mainly utilizes earthquake and slope related data, and the safety of slope stability derived from LSMAP, which is a landslide prediction program. Backyang Mt. in Busan where the landslide has already occurred, was chosen as the study area of this research. As a result of this study, the area of landslide prone zone identified by using the Newmark displacement model without earthquake factor is about 1.15 times larger than that identified by using LSMAP.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.27-40
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• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.23-33
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• 2006

In this study, application potential of SINMAP, a GIS-based landslide hazard mapping tool, is evaluated through a case study. Through the application to the severe landslide events occurred during a heavy storm in 1991 on the Mt. Dalbong area about 78 kilometers south from Seoul, SINMAP successfully spotted most landslide sites. The effects and proper ranges of three calibration parameters of SINMAP, i.e. the soil internal friction angle, the combined cohesion of tree roots and soil, and T/R, were examined through comparison of predicted landslides with the landslide inventory data. From the findings of this study, it seems that SINMAP could be used as an effective screening tool for landslide hazard mapping especially for mountain areas with fairly steep slopes and relatively thin soil layers.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.310-313
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• 2004

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from TM satellite images; and the vegetation index value from SPOT satellite images. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better prediction accuracy than probabilistic model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.12 no.2
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• pp.147-154
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• 1994

Landslides in the close residential area occur loss of life and properties. Usually soil mechanical methods and multi-statistical methods used to analysis the landslide. The factors of landslide analysis by multi-statistical methods are composite operations of geology, topography, vegetation, and climate etc. In this study, existing landslide areas are cheesed to extract the characteristics of their geographical data for adaption of Multi-statical methods. Authors give a weight to their values and analyze the some area using technique of overlap on GSIS.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.375-380
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• 2005

In Korea, most landslides are occurred during the rainy season from June to September and have a shallow failure plane parallel to the slope. For these types of rainfall-induced failures, the most important factors triggering slope unstability is not the increase of pore water pressure but the decrease of the matric suction of unsaturated soils by rainfall infiltration. So it is essential to landslide hazard assessment that defines the characteristics of infiltration in natural slopes. In this study, field measurements have been carried out in order to monitor in-situ volumetric water contents and ground water table, at several depths and locations on a natural slope. The results show that rainfall infiltration is correlated with antecedent water contents, rainfall intensity and total rainfall. The ground water table was varied sensitively by every rainfall event.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.141-143
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• 2003

The aim of this study is to evaluate the susceptibility of landslides at Clicap area, Indonesia , using a Geographic Information System (GIS). Landslide locations were identified from field surveys. The topographic and geological map were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database and lihology and fault was extracted from the geological database. Then landslide susceptibility was analyzed using the landslide-occurrence factors by likelihood methods. The results of the analysis were verified using the landslide location data. The GIS was used to analyze the vast amount of data efficiently . The results can be used to reduce associated hazards, and to plan land use and construction.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.963-978
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• 2020

Earthquakes can induce a large number of landslides and cause very serious property damage and human casualties. There are two issues in study on earthquake-induced landslides: (1) slope stability analysis under seismic loading and (2) debris flow run-out analysis. This study aims to review technical studies related to the development and application of earthquake-induced landslide models (seismic slope stability analysis). Moreover, a pilot application of a physics-based slope stability model to Mt. Umyeon, in Seoul, with several earthquake scenarios was conducted to test regional scale seismic landslide mapping. The earthquake-induced landslide simulation model can be categorized into 1) Pseudo-static model, 2) Newmark's dynamic displacement model and 3) stress-strain model. The Pseudo-static model is preferred for producing seismic landslide hazard maps because it is impossible to verify the dynamic model-based simulation results due to lack of earthquake-induced landslide inventory in Korea. Earthquake scenario-based simulation results show that given dry conditions, unstable slopes begin to occur in parts of upper areas due to the 50-year earthquake magnitude; most of the study area becomes unstable when the earthquake frequency is 200 years. On the other hand, when the soil is in a wet state due to heavy rainfall, many areas are unstable even if no earthquake occurs, and when rainfall and 50-year earthquakes occur simultaneously, most areas appear unstable, as in simulation results based on 100-year earthquakes in dry condition.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.