• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.77-88
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• 1998

Smoothing spline estimators are modified to remove boundary bias effects using the technique proposed in Eubank and Speckman (1991). An O(n) algorithm is developed for the computation of the resulting estimator as well as associated generalized cross-validation criteria, etc. The asymptotic properties of the estimator are studied for the case of a linear smoothing spline and the upper bound for the average mean squared error of the estimator given in Eubank and Speckman (1991) is shown to be asymptotically sharp in this case.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.17-29
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• 2022

Recently, the interest in integrated underground geospatial information mapping (IUGIM) to ensure the safety of underground spaces and facilities has been increasing. Because IUGIM is used in the fields of underground space development and underground safety management, the up-to-dateness and accuracy of information are critical. In this study, IUGIM and field data were classified, and the accuracy of IUGIM was improved by spatial interpolation. A spatial interpolation technique was used to process borehole data in IUGIM, and a quantitative evaluation was performed with mean absolute error and root mean square error through the cross-validation of seven interpolation results according to the technique and model. From the cross-validation results, accuracy decreased in the order of nonuniform rational B-spline, Kriging, and inverse distance weighting. In the case of Kriging, the accuracy difference according to the variogram model was insignificant, and Kriging using the spherical variogram exhibited the best accuracy.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.263-273
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• 2016

This study applied conditional merging (CM) spatial interpolation technique to obtain the satellite and in-situ composite soil moisture data. For the analysis, 24 gages of hourly in-situ data sets from the Rural Development Administration (RDA) of Korea and the satellite soil moisture data retrieved from Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) were used. In order to verify the performance of the CM method, leave-one-out cross validation was used. The cross validation result was spatially interpolated to figure out spatial correlation of the CM method. The results derived from this study are as follow: (1) The CM method produced better soil moisture map over Korean Peninsula than AMSR-E did for the over 100 days out of total 113 days considered for the analysis. (2) The method of CM showed high correlation with gage density and better performance on the western side of Korean peninsula due to high spatial gauge density. (3) The performance of CM is not affected by the non-rainy season unlike to AMSR-E data is. Overall, the result of this study indicates that the CM method can be applied for predicting soil moisture at ungaged locations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1189-1194
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• 2002

Acoustic intensity is usually estimated by the cross-spectrum of acoustic pressure at two adjacent micrphones. The cross-spectrum calculated by digital Fourier transform technique will unavoidally have leakage error since the period of signal will not be usually coincident with record length. Therefore, the acoustic intensity estimated by the conventional r]n analyzer will show distorted value. In this paper the expression of the Fourier transformed data of a harmonic signal is formulated when there is leakage error. The method to elimate the effect of leakage error from the contaminated data is also proposed. Some numerical examples show the validation of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1156-1161
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• 2001

Acoustic intensity is usually estimated by the cross-spectrum of acoustic pressure at two adjacent microphones. The cross-spectrum calculated by digital Fourier transform technique will unavoidally have leakage error since the period of signal will not be usually coincident with record length. Therefore, the acoustic intensity estimated by the conventional FFT analyzer will show distorted value. In this paper, the expression of the Fourier transformed date of a harmonic signal with a single frequency is formated when there is leakage error. The method to elimate the effect of leakage error from the contaminated data is also proposed. Some numerical examples show the validation of the proposed method.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.65-76
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• 2004

This study aims to estimate daily maximum air temperature estimated using satellite-derived surface temperature and Elevation Derivative Database (EDD). The analysis is focused on the establishment of a semi-empirical estimation technique of daily maximum air temperature through the multiple regression analysis. This tests the contribution of EDD in the air temperature estimation when it is added into regression model as an independent variable. The better correlation is shown with the EDD data as compared with the correlation without this data set. In order to provide a progressive estimation technique, we propose and compare three approaches: 1) seasonal estimation non-considering landcover, 2) seasonal estimation considering landcover, and 3) estimation according to landcover type and non-considering season. The last method shows the best fit with the root-mean-square error between 0.56$^{\circ}C$ and 3.14$^{\circ}C$. A cross-validation procedure is performed for third method to valid the estimated values for two major landcover types (cropland and forest). For both landcover types, the validation results show reasonable agreement with estimation results. Therefore it is considered that the estimation technique proposed may be applicable to most parts of South Korea.

• Economic and Environmental Geology
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• v.37 no.5
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• pp.521-532
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• 2004

The aim of this study is to evaluate the susceptibility of landslides at Kangneung area, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified from interpretation of satellite image and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. Using frequency ratio model which is one of the probability model, the relationships between landslides and related factors such as slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of wood, lithology, distance from lineament and land cover were calculated as frequency ratios. Then, the frequency ratio were summed to calculate a landslide susceptibility indexes and the landslide susceptibility maps were generated using the indexes. The results of the analysis were verified and cross-validated using actual landslide location data. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Journal of the Korea Safety Management & Science
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• v.8 no.2
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• pp.51-59
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• 2006

So far, there is no technique of quantitative evaluation on danger related to industrial accidents. Therefore, as an endeavor for obtaining technique of quantitative evaluation, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze feature of industrial accidents, a retrospective analysis was performed in 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work chosen from data related to manufacturing businesses during three years $(2002\sim2004)$ in Korea. The analysis results were very informative since those enable us to know the most important variables such as occurrence type, company size, and occurrence time which can affect injured people. Also, it is found that classification using QUEST algorithm which was performed in this study is very reliable.