• Title/Summary/Keyword: Landslide Damage

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Rock Slope Failure Analysis and Landslide Risk Map by Using GIS (GIS를 이용한 암반사면 파괴분석과 산사태 위험도)

  • Kwon, Hye-Jin;Kim, Gyo-Won
    • Journal of the Korean Geotechnical Society
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    • v.30 no.12
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    • pp.15-25
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    • 2014
  • In this study, types of rock slope failure are analyzed by considering both joint characteristics investigated on previous landslide regions located at northern part of Mt. Jiri and geographic features of natural slopes deduced from GIS. The landslide prediction map was produced by superposing the frequency ratio layers for the six geographic features including elevation, slope aspect, slope angle, shaded relief, curvature and stream distance, and then the landslide risk map was deduced by combination of the prediction map and the damage map obtained by taking account of humanity factors such as roads and buildings in the study area. According to analysis on geographic features for previous landslide regions, the landslides occurred as following rate: 88% at 330~710 m in elevation, 77.7% at $90{\sim}270^{\circ}$ in slope aspect, 93.9% at $10{\sim}40^{\circ}$ in slope angle, 82.78% at grade3~7 in shaded relief, 86.28% at -5~+5 in curvature, and 82.92% within 400m in stream distance. Approximately 75% of the landslide regions belongs to the region of 'high' or 'very high' grade in the prediction map, and 13.27% of the study area is exposed to 'high risk' of landslide.

Detection of Landslide-damaged Areas Using Sentinel-2 Image and ISODATA (Sentinel-2 영상과 자기조직화 분류기법을 활용한 산사태 피해지 탐지 - 2020년 곡성 산사태를 사례로 -)

  • KIM, Dae-Sun;LEE, Yang-Won
    • Journal of the Korean Association of Geographic Information Studies
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    • v.23 no.4
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    • pp.253-265
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    • 2020
  • As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.

The July 2, 2017, Lantian landslide in Leibo, China: mechanisms and mitigation measures

  • He, Kun;Ma, Guotao;Hu, Xiewen;Liu, Bo;Han, Mei
    • Geomechanics and Engineering
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    • v.28 no.3
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    • pp.283-298
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    • 2022
  • Landslides triggered by the combination of heavy precipitation and anthropological disturbance in hilly areas cause severe damage to human lives, properties, and infrastructure constructions. A comprehensive investigation of the influencing factors and failure mechanisms of landslides are significant for disaster mitigation and prevention. This paper utilized the combination of detailed geological investigation, physical experimental testing as well as numerical modelling to determine the failure mechanism, and proposed a countermeasures of the Lantian landslide occurred on 2, July 2017. The results reveal that the Lantian landslide is a catastrophic reactivated slide which occurred in an active tectonic region in Southwest China. Because of the unique geological settings, the fully to highly weathered basalts in the study area with well-developed fractures favored the rainwater infiltration, which is the beneficial to slide reactivation. Engineering excavation and heavy precipitation are the main triggering factors to activate the slide motion. Two failure stages have been identified in the landslide. The first phase involves a shallow mass collapse originated at the upper slopes, which extends from the road to platform at rear part, which is triggered by excavation in the landslide region. Subjected to the following prolonged rainfall from 19 June to 2 July, 2017, the pore water pressure of the slope continually increased, and the groundwater table successively rise, resulting in a significant decrease of soil strength which leads to successive large-scale deep slide. Thereinto, the shallow collapse played a significant role in the formation of the deep slide. Based on the formation mechanisms of the landslide, detailed engineering mitigation measures, involving slope cutting, anchor cable frame, shotcrete and anchorage, retaining wall and intercepting ditch were suggested to reduce the future failure risk of the landslide.

Landslide Susceptibility Mapping for 2015 Earthquake Region of Sindhupalchowk, Nepal using Frequency Ratio

  • Yang, In Tae;Acharya, Tri Dev;Lee, Dong Ha
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.34 no.4
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    • pp.443-451
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    • 2016
  • Globally, landslides triggered by natural or human activities have resulted in enormous damage to both property and life. Recent climatic changes and anthropogenic activities have increased the number of occurrence of these disasters. Despite many researches, there is no standard method that can produce reliable prediction. This article discusses the process of landslide susceptibility mapping using various methods in current literatures and applies the FR (Frequency Ratio) method to develop a susceptibility map for the 2015 earthquake region of Sindhupalchowk, Nepal. The complete mapping process describes importance of selection of area, and controlling factors, widespread techniques of modelling and accuracy assessment tools. The FR derived for various controlling factors available were calculated using pre- and post- earthquake landslide events in the study area and the ratio was used to develop susceptibility map. Understanding the process could help in better future application process and producing better accuracy results. And the resulting map is valuable for the local general and authorities for prevention and decision making tasks for landslide disasters.

Current and Future Status of GIS-based Landslide Susceptibility Mapping: A Literature Review

  • Lee, Saro
    • Korean Journal of Remote Sensing
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    • v.35 no.1
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    • pp.179-193
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    • 2019
  • Landslides are one of the most damaging geological hazards worldwide, threating both humans and property. Hence, there have been many efforts to prevent landslides and mitigate the damage that they cause. Among such efforts, there have been many studies on mapping landslide susceptibility. Geographic information system (GIS)-based techniques have been developed and applied widely, and are now the main tools used to map landslide susceptibility. We reviewed the status of landslide susceptibility mapping using GIS by number of papers, year, study area, number of landslides, cause, and models applied, based on 776 articles over the last 20 years (1999-2018). The number of studies published annually increased rapidly over time. The total study area spanned 65 countries, and 47.7% of study areas were in China, India, South Korea, and Iran, where more than 500 landslides, 27.3% of all landslides, have occurred. Slope (97.6% of total articles) and geology (82.7% of total articles) were most often implicated as causes, and logistic regression (26.9% of total articles) and frequency ratio (24.7% of total article) models were the most widely used models. We analyzed trends in the causes of and models used to simulate landslides. The main causes were similar each year, but machine learning models have increased in popularity over time. In the future, more study areas should be investigated to improve the generalizability and accuracy of the results. Furthermore, more causes, especially those related to topography and soil, should be considered and more machine learning models should be applied. Finally, landslide hazard and risk maps should be studied in addition to landslide susceptibility maps.

Review of earthquake-induced landslide modeling and scenario-based application

  • Lee, Giha;An, Hyunuk;Yeon, Minho;Seo, Jun Pyo;Lee, Chang Woo
    • 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.

The Assessment of Landslide Hazards in Gyeonggi Icheon area using GIS-based SINMAP Model Analysis (GIS기반의 SINMAP을 통한 경기도 이천지역의 산사태 위험도 분석)

  • Kwon, Ki-Bum;Lee, Hee-Chul;Chun, Jin-Soo
    • Proceedings of the Korean Geotechical Society Conference
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    • 2010.09a
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    • pp.782-789
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    • 2010
  • Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In this study, performed a GIS-based landslide hazard analysis by SINMAP(Stability Index MAPping) model in Gyeonggi Icheon area coupling with geomorphological and geological data. SINMAP model has its theoretical basis in the infinite plane slope stability model with wetness obtained from a topographically based steady state model of hydrology. To Gyeonggi Icheon area landslides hazards evaluated, these SINMAP model were analysed results while simultaneously referring to the stability index map, where lines distinguish the zones categorized into the different stability classes and a table giving summary statistics.

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IDENTIFICATION OF EROSION PRONE FOREST AREA - A REMOTE SENSING AND GIS APPROACH

  • Jayakumar, S.;Lee, Jung-Bin;Enkhbaatar, Lkhagva;Heo, Joon
    • Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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    • 2008.10a
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    • pp.251-253
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    • 2008
  • Erosion and landslide cause serious damage to forest areas. As a consequence, partial or complete destruction of vegetation occurs, which leads to many cascading problems. In this study, an attempt has been made to identify the forest areas, which are under different risk categories of erosion and landslide, in part of Eastern Ghats of Tamil Nadu. Relevantthematic maps were generated from satellite data, topographical maps, primary and secondary data and weights to each map were assigned appropriately. Weighted overlay analysis was carried out to identify the erosionprone forest areas. The result of erosion and landslide prone model reveals that 4712 ha(17%) of forest area is under high risk category and 15879 ha(58.65%) isunder medium risk category. The results of spatial modeling would be very much useful to the forest officials and conservationist to plan for effective conservation.

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Simplified Method for the Determination of Cumulative Landslide Displacement in the Event of an Earthquake using "Slide Block" Type Analyses (지진발생시 Slide Block형 분석을 이용한 누적 산사태 변위 결정 단순법)

  • Bae, Yoon-Shin
    • Journal of the Korean Geosynthetics Society
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    • v.8 no.1
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    • pp.1-10
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    • 2009
  • Earthquake induced landslides have caused tens of thousands of deaths and billions of dollars of damage during the last century alone. Determining the potential seismic hazard presented by statically stable slopes is essential for the evaluation of substantial landslide movement during an earthquake. Newmark's method for estimating landslide displacement under dynamic loading was presented and applied to two case studies. A simplified energy-based method was then be developed to estimate the Newmark's displacement.

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Landslide Susceptibility Prediction using Evidential Belief Function, Weight of Evidence and Artificial Neural Network Models (Evidential Belief Function, Weight of Evidence 및 Artificial Neural Network 모델을 이용한 산사태 공간 취약성 예측 연구)

  • Lee, Saro;Oh, Hyun-Joo
    • 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.