• The Journal of Engineering Geology
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• v.33 no.4
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• pp.639-655
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• 2023

Recently, there has been a growing demand for underground space, leading to the utilization of earth retaining walls for deep excavations. Earth retaining walls are structures that are susceptible to displacement, and their measurement and management are carried out in accordance with the standards established by the Ministry of Land, Infrastructure, and Transport. However, managing displacement through measurement can be considered similar to post-processing. Therefore, in this study, we not only predicted the horizontal displacement of a retaining wall with ground anchors installed using machine learning, but also analyzed the impact of the prediction model based on data scaling and data splitting methods while learning measurement data using machine learning. Custom splitting was the most suitable method for learning and outputting measurement data. Data scaling demonstrated excellent performance, with an error within 1 and an R-squared value of 0.77 when the anchor tensile force and water pressure were standardized. Additionally, it predicted a negative displacement compared to a model that without scaling.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.449-466
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• 2022

The establishment of a complete geological model that includes information about all the various components at a site (such as underground structures and the compositions of rock and soil underground space) is difficult, and geological modeling is a developing field. This study uses commercial software for the relatively easy composition of geological models. Our digital modeling process integrates a model of Jeju Island's 3D geological information, models of cave shapes, and information on the state of a road at the site's upper surface. Among the numerous natural caves that exist in Jeju Island, we studied the Jaeamcheon lava tube near Hallim town, and the selected site lies below a road. We developed a digital model by applying the principles of building information modeling (BIM) to the cave (CaveBIM). The digital model was compiled through gathering and integrating specific data: relevant processes include modeling the cave's shape using a laser scanner, 3D geological modeling using geological information and geophysical exploration data, and modeling the surrounding area using drones. This study developed a global-scale model of the Hallim region and a local-scale model of the Jaeamcheon cave. Cross-validation was performed when constructing the LSM, and the results were compared and analyzed.

• Proceedings of the International Union of Geodesy And Geophysics Korea Journal of Geophysical Research Conference
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• 2003.05a
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• pp.16-16
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• 2003

본 연구는 ILP(International Lithosphere Project) Task Group II-4가 진행하고 있는 상부맨틀에 대한 3차원 구조도 작성 연구의 일환으로 수행되어졌으며 구조도 작성을 위한 데이터 베이스의 구조는 task group의 표준안을 따랐다. 기존 문헌과 기존의 데이터 베이스를 통해서 획득된 자료를 이용해 동해와 그 주변을 대상으로 하는 지역의 ($32-45^{\circ}$E, $122-148^{\circ}$N) 상부 670km까지의 3차원 구조도 작성을 위한 초기 모델을 구축하였으며, 이 절차를 최대한 자동화하는 프로그램을 포트란을 이용해 만들어보았다. 연구 지역에 대한 곡율을 계산하기 위해 표준타원체 모델인 WGS84과 geoid undulation 모델인 EGM96을 사용했으며 지형 고도 자료는 GTOPO30, GLOBE 1.0, 그리고 Smith and Sandwell 데이터베이스를 사용하여 지구 중심으로부터 지표까지의 거리를 구하였다. 연구지역은 $0.25^{\circ}$간격으로 나누었으며 총 5777개의 격자점을 정의하였으며 각각의 격자점에 1차원 수직구조를 부여함으로써 3차원 모델을 구축하였다. 그리고 지형적으로나 지질학적으로 유사한 지역을 하나의 구역으로 정의하고 동일한 수직구조를 부여함으로써 모든 격자점에 1차원 수직구조를 정의하지 않도록 하였다. 본 연구에서는 지표 지질은 모델에 고려하지 않았지만 지형학적으로 의미가 있는 분지나 수평적으로 불균질성이 뚜렷한 지역을 중심으로 연구 지역의 리젼을 정의하였다. 중요 리젼에 대한 지각구조에 대해서는 기존의 문헌을 통해 모델치를 정의하였으며 지각 하부부터 상부 670km에 대한 부분은 Task Group에서 제시한 표준 모델을 이용했다. 모델을 정의하기 위해 주어진 격자점에 대한 지구 중심으로부터 지오이드까지의 거리, 지오이드로부터 지표까지의 거리를 정의해주었으며, 각 격자점의 수직구조를 정의하기 위해 깊이에 따른 각 매질의 밀도, P파의 속도, S파의 속도, P파에 대한 Q값, S파에 대한 Q값을 정의 해주었다. S파의 속도를 구하기 위해서 지구 내부 물질을 포아송 매질이라는 가정 하에, 관계식을 $Vp{\;}={\;}SQRT(3){\;}{\times}{\;}Vs$ 이용하였다. 획득한 모델치들을 이용해 동해와 동해 인근 지역에 대한 초기모델을 구축하였다.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.201-208
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• 2021

This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.

• 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 the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• Economic and Environmental Geology
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• v.19 no.3
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• pp.219-224
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• 1986

Spectral induced polarization (IP) responses for 20-degree dipping body are obtained by both numerical and scale models. The IP responses for the dipping body vary not only with current frequencies but also with resistivity ratios between the body and the surrounding medium. If the ion concentration related to polarizable reaction is constant, the resistivity of polarizable body depends only on the current frequency. This implies that the IP responses to the resistivity ratio are qualitatively equivalent to those to the current frequency. The numerical results with wide-range resistivity ratios, therefore, can be used as standard curves for the interpretation of spectral IP data.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.619-630
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• 2019

Dyslipidemia is a representative chronic disease affecting Koreans that requires continuous management. It is also a known risk factor for cardiovascular disease such as hypertension and diabetes. However, it is difficult to diagnose vascular disease without a medical examination. This study identifies risk factors for the recognition and prevention of dyslipidemia. By integrating them, we construct a statistical instrumental nomogram that can predict the incidence rate while visualizing. Data were from the Korean National Health and Nutrition Examination Survey (KNHANES) for 2013-2016. First, a chi-squared test identified twelve risk factors of dyslipidemia. We used a naïve Bayesian classifier model to construct a nomogram for the dyslipidemia. The constructed nomogram was verified using a receiver operating characteristics curve and calibration plot. Finally, we compared the logistic nomogram previously presented with the Bayesian nomogram proposed in this study.