• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.281-292
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• 2014

This study examined the accuracy that produced using various types and combinations of landslide-related factors from landslide susceptibility index maps. A database of landslide-related factors was adopted by the landslide locations that obtained from aerial photographs, and the topographic factors that derived from airborne LiDAR observations and digital maps, and various soil, forest, and land cover. Landslide susceptibility index maps were calculated by logistic regression and frequency ratio from the landslide susceptibility index. The correlation between airborne LiDAR data and digital map was shown strong similarities with one another. Landslide susceptibility index maps indicated the existence of a strong correlation and high prediction accuracy, especially when the frequency ratio and airborne LiDAR were used. Therefore, we concluded that the Airborne LiDAR will contribute to the development of effective landslide prediction methods and damage reduction measures.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.19-27
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• 2017

This research was aimed to analyze landslide susceptibility and compare the prediction accuracy using ensemble frequency ratio (FR) and logistic regression at the Inje area, Korea. The landslide locations were identified with the before and after aerial photographs of landslide occurrence that were randomly selected for training (70%) and validation (30%). The total twelve landslide-related factors were elevation, slope, aspect, distance to drainage, topographic wetness index, stream power index, soil texture, soil sickness, timber age, timber diameter, timber density, and timber type. The spatial relationship between landslide occurrence and landslide-related factors was analyzed using FR and ensemble model. The produced LSI maps were validated and compared using relative operating characteristics (ROC) curve. The prediction accuracy of produced ensemble LSI map was about 2% higher than FR LSI map. The LSI map produced in this research could be used to establish land use planning and mitigate the damages caused by disaster.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.75-87
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• 2005

This study aims at predicting and mapping of the landslide susceptibility in the Geumho river watershed using GIS and Remote Sensing techniques. We constructed the spatial database of affecting factors such as slope angle, slope aspect, lithology, landuse, and vegetation index (NDVI) at a $30m{\times}30m$ resolution. The landslide susceptibility of the study area was predicted through overlay analysis and adding up estimation matrix, and the predicted map of landslide susceptibility with six categories (stable, very low, low, moderate, high, very high) was constructed. As the results, it showed that the very high susceptibility zones made up approximately 0.3% of the total study area, and these zones were mainly distributed in the forest area with the high slope angle and low vegetation index.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.61-69
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• 2010

The collapse of a slope or cut embankment brings much damage to life and property. Accordingly, it is very important to analyze the spatial distribution by calculating the landslide susceptibility in the estimation of the risk of landslide occurrence. The heuristic, statistic, deterministic, and probabilistic methods have been introduced to make landslide susceptibility maps. In many cases, however, the reliability is low due to insufficient field data, and the qualitative experience and knowledge of experts could not be combined with the quantitative mechanical?analysis model in the existing methods. In this paper, new modeling method for a probabilistic landslide susceptibility analysis combined Bayesian Network with ontology model about experts' knowledge and spatial data was proposed. The ontology model, which was made using the reasoning engine, was automatically converted into the Bayesian Network structure. Through conditional probabilistic reasoning using the created Bayesian Network, landslide susceptibility with uncertainty was analyzed, and the results were described in maps, using GIS. The developed Bayesian Network was then applied to the test-site to verify its effect, and the result corresponded to the landslide traces boundary at 86.5% accuracy. We expect that general users will be able to make a landslide susceptibility analysis over a wide area without experts' help.

• Journal of Climate Change Research
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• v.3 no.1
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• pp.39-50
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• 2012

KMA(Korea Meteorological Administration) projected that annual mean temperatures of South Korea will rise $3.8^{\circ}C$ and the annual total precipitation will increase by 17 percent by 2100. Rainfall is concentrated during the summer in South Korea. Thus the risk of landslide by heavy rain is expected to increase. After the landslide of Mt. Umyeon occurred in July 2011, disaster of forest sector is highlighted. Therefore vulnerability assessment of landslide is urgent. However, vulnerability assessment based on local governments was not done yet. In this study, we assess vulnerability of landslide by heavy rain for local governments. We used several scenarios to consider uncertainty of climate change. Through this study, local governments can use the results to establish adaptation plans. Also, the results could be used to decrease vulnerability of landslide.

• Economic and Environmental Geology
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• v.43 no.4
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• pp.381-391
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• 2010

Probabilistic prediction methods of landslides which have been developed in recent can be reliable with premise of detailed survey and analysis based on deep and special knowledge. However, landslide susceptibility should also be analyzed with some reliable and simple methods by various people such as government officials and engineering geologists who do not have deep statistical knowledge at the moment of hazards. Therefore, this study suggests an evaluation chart of landslide susceptibility with high reliability drawn by accurate statistical approaches, which the chart can be understood easily and utilized for both specialists and non-specialists. The evaluation chart was developed by a quantification method based on canonical correlation analysis using the data of geology, topography, and soil property of landslides in Korea. This study analyzed field data and laboratory test results and determined influential factors and rating values of each factor. The quantification analysis result shows that slope angle has the highest significance among the factors and elevation, permeability coefficient, porosity, lithology, and dry density are important in descending order. Based on the score assigned to each evaluation factor, an evaluation chart of landslide susceptibility was developed with rating values in each class of a factor. It is possible for an analyst to identify susceptibility degree of a landslide by checking each property of an evaluation factor and calculating sum of the rating values. This result can also be used to draw landslide susceptibility maps based on GIS techniques.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1047-1060
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• 2017

In recent years, global warming has been continuing and abnormal weather phenomena are occurring frequently. Especially in the 21st century, the intensity and frequency of hydrological disasters are increasing due to the regional trend of water. Since the damage caused by disasters in urban areas is likely to be extreme, it is necessary to prepare a landslide susceptibility maps to predict and prepare the future damage. Therefore, in this study, we analyzed the landslide vulnerability using the logistic model and assessed the management plan after the landslide through the field survey. The landslide area was extracted from aerial photographs and interpretation of the field survey data at the time of the landslides by local government. Landslide-related factors were extracted topographical maps generated from aerial photographs and forest map. Logistic regression (LR) model has been used to identify areas where landslides are likely to occur in geographic information systems (GIS). A landslide susceptibility map was constructed by applying a LR model to a spatial database constructed through a total of 13 factors affecting landslides. The validation accuracy of 77.79% was derived by using the receiver operating characteristic (ROC) curve for the logistic model. In addition, a field investigation was performed to validate how landslides were managed after the landslide. The results of this study can provide a scientific basis for urban governments for policy recommendations on urban landslide management.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.39-46
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• 2007

Most areas in Kangwon Province are mountainous and vulnerable to landslide due to the rainy season in summer and the localized torrential downpour triggered by abnormal climate. In particular, the rainfall is one of direct reasons for landslide. In accordance with the analysis of the relevance between the landslide areas and the accumulated rainfall for four months, there are severe damages of landslide to the areas having more than 1,100 mm of rainfall during three(3) months. Further, it indicates that the more the accumulated rainfall is the greater the size of landslide. These analyses show that the rainfall causes the possible and potential landslide in the vulnerable areas. And also, it means that there exist strong possibilities of landslide even in the areas of lower vulnerability if the amount of rainfall is above certain standard level. Accordingly, in this study we stored the GIS database on the causes and factors of landslide in the southern parts of Kangwon province and conducted simulations on the change of distribution of vulnerable areas by varying the rainfall conditions and by using the evaluation data of landslide vulnerability. As such a result, we found that the landslide could potentially occur if the amount of rainfall is 200 mm and more.