• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• 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.

• 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.

• 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.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.687-699
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• 2018

Understanding rain infiltration into the ground is an important feature of landslide risk evaluation. In this study, a landslide risk index for the study area is suggested, wherein the result of the landslide risk evaluation, based on the factor of safety (FS), is used. The landslide risk index is a landslide risk prediction index that utilizes the saturated depth ratio of the ground. Based on the landslide risk result for the study area, it was found that the FS was first to decrease. However, it gradually became convergent over the 50-year rainfall intensity study period, a result that is similar to the relationship between the saturated depth ratio and soil thickness. Moreover, saturated depth was also found to be deeper on gentle slopes than steep slopes. As such, the landslide risk index, based on the Inhu-ri study result, is thus suggested. Additionally, the suggested landslide risk index was compared and analyzed against the rainfall intensity of previous landslide experience. Results thus revealed that almost all landslides that occurred were over 0.7, which is the second grade, based on the landslide risk index.

• The Journal of Engineering Geology
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• v.23 no.3
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• pp.305-314
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• 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
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• v.22 no.2
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• pp.119-127
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• 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.

• The Journal of Engineering Geology
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• v.22 no.3
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• pp.283-291
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• 2012

Various studies regarding the prediction of landslides are underway internationally. Research into disaster prevention with regard to debris flows is a particular focus of research because this type of landslide can cause enormous damage over a short period. The objective of this study is to determine the hazard susceptibility of debris flow via predictions of surface water concentrations based on the concept that a debris flow is similar to a surface water flow, as it is influenced by mountain topography. This study considered urban areas affected by large debris flows or landslides. Digital mapping (including the slope and upslope contributing areas) and the wetness index were used to determine the relevant topographic factors and the hydrology of the area. We determined the hazard susceptibility of debris flow by predicting the surface water concentration based on the topography of the surrounding mountainous terrain. Results obtained using the distinct element method were used to derive a correlation equation between the weight and the impact force of the debris flow. We consider that in using a correlation equation, this method could assist in the effective installation of debris-flow-prevention structures.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.