• Journal of Environmental Policy
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• v.12 no.3
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• pp.21-47
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• 2013

The aim is to analysis landslide vulnerability in Inje, Korea, using GCI(Geospatial Correlative Integration) and probability rainfalls based on geographic information system (GIS). In order to achieve this goal, identified indicators influencing landslides based on literature review. We include indicators of exposure to climate(rainfall probability), sensitivity(slope, aspect, curvature, geology, topography, soil drainage, soil material, soil thickness and soil texture) and adaptive capacity(timber diameter, timber type, timber density and timber age). All data were collected, processed, and compiled in a spatial database using GIS. Karisan-ri that had experienced 470 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data, while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 3-day cumulative rainfalls of 449 mm. Results show that number of slope has comparatively strong influence on landslide damage. And inclination of $25{\sim}30^{\circ}C$, the highest correlation landslide. Improved previous landslide vulnerability methodology by adopting GCI. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing landslide mitigation policies.

• Spatial Information Research
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• v.8 no.1
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• pp.141-153
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• 2000

The purpose of this study is to develop and apply spatial landslide information system using Geographic information system (GIS) in concerned with spatial data. Landslide locations detected from interpretation of aerial photo and field survey, and topographic , soil , forest , and geological maps of the study area, Yongin were collected and constructed into spatial database using GIS. As landslide occurrence factors, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. In addition, landslide damageable objects such as building, road, rail and other facility were extracted from the topographic database. Landslide susceptibility was analyzed using the landslide occurrence factors by probability, logistic regression and neural network methods. The spatial landslide information system was developed to retrieve the constructed GIS database and landslide susceptibility . The system was developed using Arc View script language(Avenue), and consisted of pull-down and icon menus for easy use. Also, the constructed database can be retrieved through Internet World Wide Web (WWW) using Internet GIS technology.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

• Journal of Climate Change Research
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• v.2 no.1
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• pp.43-54
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• 2011

Recently, due to severe rainfall by the global climate change, natural disasters such as landslide had also been increased rapidly all over the world. Therefore, it has been very necessary to assess vulnerability of landslide and prepare adaptation measures to future climate change. In this study, we employed sensitivity, exposure and adaptative capacity as criteria for assessing the vulnerability of landslide due to climate change. Spatial database for the criteria was constructed using GIS technology. And vulnerability maps on the entire Korea of past and future were made based on the database. As a result, highly vulnerable area for landslide was detected in most area of Gangwon-do, the east of Gyeonggi-do, and southeast of Jeollanam-do, and the southwest of Gyeongsangnam-do. The result of landslide vulnerability depends on time shows that degree of very low class and low class were decreased and degree of moderate, high, and very high were increase from past to the future. Especially, these three classes above low class were significantly increased in the result of far future.

• Economic and Environmental Geology
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• v.47 no.1
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• pp.49-59
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• 2014

Rainfall-induced landslides are caused by reduction of effective stress and shear strength due to rainfall infiltration. In order to analyze the susceptibility of landslides, the statistical analysis approach has been used widely but this approach has the limitation which cannot take into account of landslide triggering mechanism. Therefore, the physically based model which can consider the process of landslide occurrence was proposed and commonly used. However, the most previous physically based model analyses evaluate and consider the strength characteristics for saturated soil only in the susceptibility analysis. But the strength parameters for unsaturated soil such as matric suction should be considered with the strength parameters for saturated soil since the shear strength in unsaturated soil also plays important role in the stability of slope. Consequently this study suggested the modified physically based slope model which can evaluate strength characteristics for both of saturated and unsaturated soils. In addition, this study evaluated the thickness of saturated part in slope with rainfall intensity and hydraulic characteristics of slope on the basis of physically based model. In order to evaluate the feasibility, the proposed model was applied to practical example in Jinbu area, Gangwon-do, which was experienced large amount of landslides in July 2006. The ROC graph analysis was used to evaluate the validation of the model, and the analysis results were compared with the results of the previous analysis approach.

• Journal of Climate Change Research
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• v.4 no.2
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• pp.95-104
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• 2013

Average cumulative precipitation in summer have increased by 350 mm compared with 1980s. As precipitation is expected to increase, the risk of landslides by heavy rainfall also is expected to rise. Therefore, establishment of adaptation plan for landslides is urgently needed. In 2011, Korea Ministry of Environment(KME) conducted vulnerability assessment to support establishment of adaptation plan for local governments. However, the result of vulnerability assessment had three limitations. First, KME didn't use standard scenario of Korea Meteorological Administration(KMA). Second, They conducted same standardization method for all variables. Third, They derived relative vulnerability which is not quantitative. The purpose of this study is to improve the limitations of existing vulnerability assessment and identify quantitative criteria to ensure scientific reliability. To achieve this purpose, we carried out three ways of advancement. First, application of new climate scenario, which is RCP 8.5 from KMA. Second, improvement of variables of vulnerability assessment. Third, derivation of quantitative criteria of vulnerability. The findings can support establishment of adaptation plan for local governments more effectively.

• Economic and Environmental Geology
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• v.48 no.2
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• pp.177-185
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• 2015

The objective of this study is to evaluate landslide hazard susceptibility and produce the landslide hazard maps for soil slope using checklists as a preliminary investigation method. Tables, proposed by NDMI (National Disaster Management Institute), are applied for slope stability assessment, and are comprised of checklists on soil slopes. Database including engineering properties of soil is constructed through the field survey and results from previous studies for The Mt. Hwangryoeng area at center of Busan. All data related to creating the thematic maps was carried out using ArcGIS 10.0. Results from using this method indicated that soil slope are evaluated from very stable to stable. Moderate stability has been partially presented along the edge of mountain. Results from landslide hazard maps can be used to prevent damage from landslides and facilitate appropriate land use planning.

• The Journal of Engineering Geology
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• v.26 no.1
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• pp.23-32
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• 2016

This study evaluates landslide susceptibility using statistical analysis by SVM (support vector machine) and the illite index of clay minerals extracted from ASTER(advanced spaceborne thermal emission and reflection radiometer) imagery which can be use to create mineralogical mapping. Landslide locations in the study area were identified from aerial photographs and field surveys. A GIS spatial database was compiled containing topographic maps (slope, aspect, curvature, distance to stream, and distance to road), maps of soil properties (thickness, material, topography, and drainage), maps of timber properties (diameter, age, and density), and an ASTER satellite imagery (illite index). The landslide susceptibility map was constructed through factor correlation using SVM to analyze the spatial database. Comparison of area under the curve values showed that using the illite index model provided landslide susceptibility maps that were 76.46% accurate, which compared favorably with 74.09% accuracy achieved without them.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.57-70
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• 2012

The aim of this study is to analyse the landslide susceptibility and the future hazard in Inje, Korea using probability rainfalls and artificial neural network (ANN) environment based on geographic information system (GIS). Data for rainfall probability, topography, and geology were collected, processed, and compiled in a spatial database using GIS. Deokjeok-ri that had experienced 694 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 1-day rainfall of 202 mm or 3-day cumulative rainfalls of 449 mm.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.