• Journal of the Korean Geotechnical Society
• /
• v.39 no.6
• /
• pp.5-12
• /
• 2023

Proactive assessment of landslide susceptibility is necessary for minimizing casualties. This study proposes a methodology for classifying the landslide safety factor using a classification algorithm based on machine learning techniques. The high-risk area model is adopted to perform the classification and eight geotechnical parameters are adopted as inputs. Four classification algorithms-namely decision tree, k-nearest neighbor, logistic regression, and random forest-are employed for comparing classification accuracy for the safety factors ranging between 1.2 and 2.0. Notably, a high accuracy is demonstrated in the safety factor range of 1.2~1.7, but a relatively low accuracy is obtained in the range of 1.8~2.0. To overcome this issue, the synthetic minority over-sampling technique (SMOTE) is adopted to generate additional data. The application of SMOTE improves the average accuracy by ~250% in the safety factor range of 1.8~2.0. The results demonstrate that SMOTE algorithm improves the accuracy of classification algorithms when applied to geotechnical data.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_1
• /
• pp.1303-1322
• /
• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• Economic and Environmental Geology
• /
• v.50 no.3
• /
• pp.239-250
• /
• 2017

Physically based landslide susceptibility analysis has been recognized as an effective analysis method because it can consider the mechanism of landslide occurrence. The physically based analysis used the slope geometry and geotechnical properties of slope materials as input. However, when the physically based approach is adopted in regional scale area, the uncertainties were involved in the analysis procedure due to spatial variation and complex geological conditions, which causes inaccurate analysis results. Therefore, probabilistic method have been used to quantify these uncertainties. However, the uncertainties caused by lack of information are not dealt with the probabilistic analysis. Therefore, fuzzy set theory was adopted in this study because the fuzzy set theory is more effective to deal with uncertainties caused by lack of information. In addition, the vertex method and Monte Carlo simulation are coupled with the fuzzy approach. The proposed approach was used to evaluate the landslide susceptibility for a regional study area. In order to compare the analysis results of the proposed approach, Monte Carlo simulation as the probabilistic analysis and the deterministic analysis are used to analyze the landslide susceptibility for same study area. We found that Fuzzy Monte Carlo simulation showed the better prediction accuracy than the probabilistic analysis and the deterministic analysis.

• Journal of Korean Society for Geospatial Information Science
• /
• v.15 no.3
• /
• pp.69-76
• /
• 2007

Kangwondo area is mountainous and landslide is easily happened easily during the rainy period in summer time. Especially, when there is torrential downpour caused by the unusual weather change, there will be greater possibility to see landslide. Another reason behind landslide is the continuous forest fire in these several years. Since the surface of the earth has been changed by the fire, when rainfall comes, landslide just happens easily. Also, it is reported that landcover condition, excepted rainfall condition, is the most effect for determining landslide susceptibility area. In this study, it is determined a landslide vulnerable area and landcover information is extracted from four satellite image(Landsat TM), about the landslide vulnerable area, which is pictured for each year. And which distribution change is analyzed. also, NDVI picture is made and distribution change of vegetation vitality is analyzed to study that change of landcover have a effect on landslide. As a result, could know that forest and NDVI are decreasing in landslide vulnerable area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.19 no.3
• /
• pp.273-281
• /
• 2001

The landslide occurrence in Sam-Chuck area was analyzed through Geo-Spatial Information System and AHP(Analytic Hierarchy Process). Among many factors which causes landslide, terrain slope, terrain aspect, lithology, soil texture and vegetation arc taken as input data from existing maps and constructed as a database. These factors are determined by each environmental factor by environmental and geological characters in the study area, and the rating and weight about factor are input using AHP. Possible areas for landslide have been extracted by overlaying each layers. Finally, the estimated results are compared with real landslide sites to know which factor is the most effective for landslide. The results showed that lithology and soil factor have high susceptibility in Sam-Chuck area.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.701-701
• /
• 2004

• Geomechanics and Engineering
• /
• v.25 no.1
• /
• pp.1-16
• /
• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Korean Journal of Remote Sensing
• /
• v.34 no.5
• /
• pp.721-738
• /
• 2018

The purpose of this study is to create landslide vulnerability using frequency ratio (FR) and evidential belief functions (EBF) model which are two methods of probability model and to select appropriate model for each region through comparison of results in Sacheon-myeon and Jumunjin-eup of Gangneung. 762 locations in Sacheon-myeon and 548 landscapes in Jeonju-eup were constructed based on the interpretation of aerial photographs. Half of each landslide point was randomly selected for modeling and remaining landslides were used for verification purposes. Twenty landslide-inducing factors classified into five categories such as topographic elements, hydrological elements, soil maps (1:5,000), forest maps (1:5,000), and geological maps (1:25,000) were considered for the preparation of landslide vulnerability in the study. The relationship between landslide occurrence and landslide inducing factors was analyzed using FR and EBF models. The two models were then verified using the AUC (curve under area) method. According to the results of verification, the FR model (AUC = 81.2%) was more accurate than the EBF model (AUC = 78.9%) at Jeonjun-eup. In the Sacheon-myeon, the EBF model (AUC = 83.6%) was more accurate than the FR model (AUC = 81.6%). Verification results show that FR model and EBF model have high accuracy with accuracy of around 80%.

• The Journal of Engineering Geology
• /
• v.23 no.3
• /
• pp.305-314
• /
• 2013

Recent international and national research trends in landslide hazards were analyzed by performing a literature search of relevant scientific journals. For obtaining data from Korea, we used 'Information for Environmental Geology' (IEG), which covers 17 journals in the field of environmental geology. A total of 54 articles related to landslide hazards were found in 5 journals published in the period 2000-2012. The most common topic was landslide prediction or susceptibility (29 articles), followed by landslide mechanisms. For international information, we analyzed 1,851 articles from the 'Web Of Science' published from 2003 to the present. Researchers in Italy have published the greatest number of papers in this field, while papers from Korea rank first in terms of the citation index.

• Korean Journal of Environment and Ecology
• /
• v.22 no.2
• /
• pp.119-127
• /
• 2008

This research managed to establish the space information on incidence factors of landslide targeting Bukhansan National Park and aimed at suggesting a basic data for disaster prevention of a landslide for the period to come in Bukhansan National Park through drawing up the map indicating vulnerability to a landslide and ecological risks by the use of overlay analysis and adding-up estimation matrix analysis methods. This research selected slope angle, slope aspect, slope length, drainage, vegetation index(NDVI) and land use as an assessment factor of a landslide and constructed the spatial database at a level of '$30m\times30m$' resolution. The analysis result was that there existed high vulnerability to a landslide almost all over Uidong and Dobong valleys. As for ecological risks, Dobong valley, Yongueocheon valley, Jeongneung valley and Pyeongchang valley were analyzed to be higher, so it is judged that the impact on a landslide risk should be also considered in time of establishing a management plan for these districts for the time to come.