• 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.

• Economic and Environmental Geology
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• v.33 no.2
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• pp.129-138
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• 2000

The various types of geohazards like landslides resulted from civil construction (i.e. highway construction) must of analysed considering all the possible influential factor systematically. Thus, by using GIS, slope stability can be evaluated, and it can be used as a data for further detailed investigation. So the aim of this study is to present a data for decision making in selecting suitable point for remediation. For analysing slope instability, through appropriate definition and classification, landslide mechanism must be understood. In building GIS model, the selection of appropriate factors and their rating system should be made. For this, the characteristics and the mechanism of landslide have to be understood. And suitable coverage should be chosen for the model considering the slope conditions. In this study, field investigation in lst and 2nd Section, Chung-ang highway was carried out. From the field data, GIS model on slope instability was created. 5 coverages were used for it. From the result of this study, 12 unstable sections were found out and more detailed investigation is needed there.

• The Journal of Engineering Geology
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• v.19 no.1
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• pp.99-108
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• 2009

Since the preexisting evaluation methods of landslide susceptibility take somehow long time to determine the slope stability based on the field survey and laboratory analysis, there are several problems to acquire immediate evaluation results in the field. In order to overcome the previously mentioned problems and incorrect evaluation results induced by some subjective evaluation criteria and methods, this study tried to develop a method of landslide susceptibility by a quantitative and objective evaluation approach based on the field survey. Therefore, this study developed an evaluation chart for landslide susceptibility on natural terrain using the AHP analysis method to predict landslide hazards on the field sites. The AHP analysis was performed by a questionnaire to several specialists who understands mechanism and influential factors of landslide. Based on the questionnaire, weighting values of criteria and alternatives to influence landslide triggering were determined by the AHP analysis. According to the scoring results of the analysed weighting values, slope angle is the most significant factor. Permeability, water contents, porosity, lithology, and elevation have the significance to the landslide susceptibility in a descending order. Based on the assigned scores of each criterion and alternatives of the criteria, an evaluation chart for landslide susceptibility was suggested. The evaluation chart makes it possible for a geologist to evaluate landslide susceptibility with a total score summed up each alternative score.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.585-596
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• 2022

This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.