• The Korean Journal of Ecology
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• v.20 no.2
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• pp.145-151
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• 1997

A census of the forest vegetation of Mt. Kaya National Park was taken with respect to the medthods of ordination and classification. Eight groups were classified by cluster analysis : Quercus mongolica, Q. mongolica-Sasa borealis, Q. serrata, Q. variabilis, Carpinus laxiflora, Cornus controversa, Fraxinus mandshurica and Pinus densiflora. Among them, the Q. mongolica group and Q. mongolica-S. borealis group, the F. mandshurica group and C. controversa group showed similar floristic compositions to each other. The interrelationship between the floristic compositon of the vegetation and soil environment was analyzed by PCA, the Q. mongolica group and the Q. mongolica-S. borealis group were distributed in areas of high CEC and total nitrogen content, while the Q. serrata. Q. variabilis. C. laxiflora and Pinus densiflora groups formed their communities in the more moderate areas of pH, soil moisture and soil organic matter. F. mandshurica and C. controversa groups were distributed in the areas of high soil moisture, soil organic matter, pH and available phosphate.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.413-433
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• 2010

South Dakota is an important agricultural state in the United States with annual cash receipts from agricultural products exceeding $9 billion dollars. This production is possible because of large areas of productive soils. This publication describes the general characteristics and qualities of the major soil groups recognized in South Dakota. The soil forming factors are briefly described, soil classification is introduced, and the genesis of typical Udalf and Ustoll soils are discussed. Soil management issues impacting the use of SD soils are considered. Long-term (>70 yrs) cultivation has significantly reduced surface soil organic carbon levels (>30% reduction) when compared to non-cultivated soil. Soil test phosphorus levels significantly increased in cultivated fields due to commercial P fertilization. The major long-term production problems for SD soils are conservation of soil moisture, organic matter and nitrogen losses, fertility management, and wind and water erosion control.

• Journal of Soil and Groundwater Environment
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• v.18 no.7
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• pp.41-53
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• 2013

The study is to propose the optimal separation technique of heavy metals (Pb and Zn) contaminated in soil for improving the removal efficiency by various applicable techniques. The heavy metal contaminated soil samples near abandoned mine X-1 and X-2 were used for the study. Firstly, the wet classification process was shown more than 80% of removal efficiency for lead and zinc. Meanwhile, the magnetic separation process was shown low removal efficiency for lead and zincs because those heavy metals were non-magnetic materials. For the next step, the flotation separation process was shown approximately 24.4% of removal efficiency for zinc, while the gravity concentration process was shown approximately 57% of removal efficiency for lead, and 19.9% of removal efficiency for zinc, respectively. Therefore, zinc contaminated in soil would be effectively treated by the combination technique of the wet classification and the flotation technique. Meanwhile, lead contaminated in soil would be effectively treated by the combination technique of the wet classification process and the flotation process. Furthermore, the extraction of organic matter was shown more effective with aeration, 3% of hydrogen peroxide and 3% of lime such as calcium hydroxide.

• Journal of the Korean Geotechnical Society
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• v.39 no.2
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• pp.5-17
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• 2023

In this study, a classification and uniaxial compression test of soil was conducted on 15 collapsed sites to use ground improvement with excellent protection effect owing to the increase of localized heavy rain in Korea. The Casagrande method and fall cone test were performed on the field soil to derive an expression for comparing liquid limit and plastic limit values, soil classification, and correlation between each other. By deriving the optimal mixing ratio of the ground improvement agent using uniaxial compressive strength for each soil classification, the classification of the fine-grained soil was not clear owing to the proficiency difference and test error. However, after classifying using the fall cone test, it was possible to suggest a clear optimal mixing ratio.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.247-258
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• 2022

A field velocity resistivity probe (FVRP) can measure compressional waves, shear waves and electrical resistivity in boreholes. The objective of this study is to perform the soil classification through a machine learning technique through elastic wave velocity and electrical resistivity measured by FVRP. Field and laboratory tests are performed, and the measured values are used as input variables to classify silt sand, sand, silty clay, and clay-sand mixture layers. The accuracy of k-nearest neighbors (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM), selected to perform classification and optimize the hyperparameters, is evaluated. The accuracies are calculated as 0.76, 0.91, 0.94, and 0.88 for KNN, NB, RF, and SVM algorithms, respectively. To increase the amount of data at each soil layer, the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to overcome imbalance in the dataset. The CTGAN provides improved accuracy in the KNN, NB, RF and SVM algorithms. The results demonstrate that the measured values by FVRP can classify soil layers through three kinds of data with machine learning algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1C
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• pp.63-74
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• 2008

Site response analyses were performed based on equivalent linear technique using the local geologic and dynamic site characteristics, which include soil profiles, shear wave velocity profiles and depth to bedrock for 125 sites collected in Korean Peninsula. From the results of site response analyses, 2-parameters site classification system based on the combination of mean shear wave velocity of soil and depth to bedrock was newly recommended for regions of shallow bedrock depth in Korea. First, as the borders of bedrock depth (H) for site classification were determined as 10m and 20m, the soil sites were divided into 3 classes as $H_1$, $H_2$ and $H_3$ sites. And then, the 3 site classes were subdivided into 7 classes based on the mean shear wave velocity of soil ($V_{s,soil}$). The feasibility of new site classification system was verified and the representative site coefficients ($F_a$ and $F_v$) and design response spectrum were suggested by analyzing uniform trend and dispersion of site coefficients for each site class. The suggested site coefficients and the regression curves present the nonlinear characteristics of soils according to the change of rock outcrop acceleration with uniform trend effectively. From the comparison between the mean values of response spectrum which was acquired from the site response analysis and the suggested design response spectrum, there was a little difference in some of site classes and it was verified to adjust the integration interval to make it more suitable for the site condition in Korea.

• The Korean Journal of Ecology
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• v.16 no.1
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• pp.1-15
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• 1993

Ordination and classification techiques were used to analyze the forest communities and to examine the integration problem of community-to-ecological species group in mt. moak provincial park of korea. phytosociological classiication based on floristic composition produced seven commuities of zelkova serrata, carpinus densiflora. These seven communities were well discriminated in the two-dimensional analyses of soil moisture, soil organic matter content and temperature(elevation), eciprocally, and in three-dimensional space of the three environmental factors also. They corresponded to seven ecological groups derived from the distribution pattern analysis of species populations in this mountain.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.95-118
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• 2010

This study aims to investigate the spatial distribution of major soil types in Korea, and to assess the ability to predict soil distribution using environmental variables. A classification tree method was used to assess soil predictability. While the great soil groups can give more intuitive understandings on their spatial distributions, its predictability using environmental factors is much lower than that of the great groups. The most important factor to determine the spatial distribution of major soil types is the geomorphological characteristic of Korea that shows distinctive morphological difference between mountains and plains. Spatial distribution of climatic variables and catenary soil sequence along slopes play additional roles in determining the distribution of soil types. The classification tree models resulted in 35-75% of prediction accuracy, depends on the combination of different environmental variables brought in the models. While geomorphological variables are the best predictors for the great groups, climatic variables perform better for the great soil groups.