• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• The Journal of Engineering Geology
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• v.21 no.3
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• pp.247-257
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• 2011

In this study the infinite slope model, one of the physical landslide models has been suggested to evaluate the susceptibility of the landslide. However, applying the infinite slope model in regional study area can be difficult or impossible because of the difficulties in obtaining and processing of large spatial data sets. With limited site investigation data, uncertainties were inevitably involved with. Therefore, the probabilistic analysis method such as Monte Carlo simulation and the GIS based infinite slope stability model have been used to evaluate the probability of failure. The proposed approach has been applied to practical example. The study area in Boeun area been selected since the area has been experienced tremendous amount of landslide occurrence. The geometric characteristics of the slope and the mechanical properties of soils like to friction angle and cohesion were obtained. In addition, coefficient of variation (COV) values in the uncertain parameters were varied from 10% to 30% in order to evaluate the effect of the uncertainty. The analysis results showed that the probabilistic analysis method can reduce the effect of uncertainty involved in input parameters.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.11-20
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• 2007

Most natural disasters in Korea are caused by meteorological natural phenomena, which include storms, heavy rains, heavy snow, hail, tidal waves, and earthquakes. Rainfall is the most frequent cause of disasters and accounts for about 80% of all disasters. Particularly in recent years, Korea has seen annual occurrences of natural disasters associated with landslides (slope and retaining wall collapse and burying) due to meteorological causes from the increasing intensity of heavy rains including local heavy rainfalls. In Korea, it is critical to analyze the characteristics of landslides according to rainfall characteristics and to take necessary and proper measures for them. This study assessed the possibility of landslides in the Gangwon region with a geographic information system by taking into account the inducer factors of landslides and the maximum continuous rainfall of each area. It also analyzed areas susceptible to landslides and checked the distribution of landslide-prone areas by considering the rainfall characteristics of those areas.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.27-39
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• 2008

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 summer, landslides frequently occur due to local torrential rains and storms. It is critical to predict the potential areas of landslides in advance and to take preventive measures to minimize consequences and to protect property and human life. The previous study on landslides mostly focused on identifying the causes of landslides in the areas where they occurred, and on analyzing landslide vulnerability around the areas without considering rainfall conditions. Thus there were not enough evaluations of the direct risk of landslides to human life. In this study, potentially risky areas for landslides were identified using the GIS data in order to evaluate direct risk on farmlands, roads, and artificial structures that were closely connected to human life. A map of landslide risk was made taking into account rainfall conditions, and a land use map was also drawn with satellite images and digital maps. Both maps were used to identify potentially risky areas for landslides.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.243-255
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• 2003

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and ${\gamma}$ operator shows the better prediction power than the results using max and min operators from the validation procedure.

• Journal of the Korean Geotechnical Society
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• v.31 no.6
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• pp.59-69
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• 2015

Recently, landslide and debris-flow disasters caused by severe rain storms have frequently occurred. Many researches related to landslide susceptibility analysis and debris-flow hazard analysis have been conducted, but there are not many researches related to mobilization analysis for landslides transforming into debris-flow in slope areas. In this study, statistical analyses such as discriminant analysis and logistic regression analysis were conducted to develop a mobilization criterion using geomorphological and geological factors. Ten parameters of geomorphological and geological factors were used as independent variables, and 466 cases (228 non-mobilization cases and 238 mobilization cases) were investigated for the statistical analyses. First of all, Fisher's discriminant function was used for the mobilization criterion. It showed 91.6 percent in the accuracy of actual mobilization cases, but homogeneity condition of variance and covariance between non-mobilization and mobilization groups was not satisfied, and independent variables did not follow normal distribution, either. Second, binomial logistic analysis was conducted for the mobilization criterion. The result showed 92.3 percent in the accuracy of actual mobilization cases, and all assumptions for the logistic analysis were satisfied. Therefore, it can be concluded that the mobilization criterion for debris-flow using binomial logistic regression analysis can be effectively applied for the prediction of debris-flow hazard analysis.

• 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.

• 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.

• Spatial Information Research
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• v.9 no.1
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• pp.125-135
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• 2001

Ground failure such as landslide, rock fall land subsidence by heavy rainfall have damaged to people and property. Especially, the damage to important facility such as dam, bridge, tunnel and industrial complex may be possible. Therefore the ground failure must be assessed and counter plan must be prepared. So, the object of this study is to develop the spatial information system for regional ground stability assessment. For this, the topographic, geologic, soil, forest, land use, rainfall frequency map, and satellite image near 40 dams were collected and constructed to the spatial information system. The spatial information system was developed using Avenue in ArcView 3.2 environment and consists of pull down menus and icons. For application of the spatial information system, regional ground stability was assessed in Andong dam. The assessment was ground failure susceptibility and possibility. The spatial information can be used for regional ground stability assessment, prevention and mitigation of hazard, and management of ground as basic data.