• Korean Journal of Agricultural Science
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• v.38 no.2
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• pp.325-330
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• 2011

In order to revive the ecological function of degraded rivers, a total restoration plan for riverbeds and riparians needs to be developed. Previous evaluations for rivers were mainly focused on the river's physical structures. Therefore, this research has developed indicators to evaluate a riparian restoration considering biodiversity. Through literature and previous cases review, 4 fields and 13 indicators are selected for the evaluation. Four fields are biodiversity, habitat diversity, connectivity and habitat functionality. In the biodiversity field, 4 indicators of the exuberant extent of herbaceous vegetation and their diversity, the exuberant extent of shrub and woody plants and their diversity, the number of plant communities and naturalized plants are included. Habitat diversity are comprised of 4 indicators of the longitudinal continuity of vegetation, the mixture of plant communities, the extent of plant type color fruit abundance and the distribution of vegetation. Connectivity includes 3 indicators of target distribution, the shore slope of low water channels and the extent of artificial embankment materials. Habitat functionality has 2 indicators of the status of food supply plants and the habitat functionality. The value weighting for the fields and indicators has been calculated based on the AHP(Analytic Hierarchy Process) method. 50 experts were surveyed with quantifiable questionnaire, among them 43 experts have more than 10 yesrs experiences in the nature restoration field. The selected and weighted indicators have been tested to the 12 sections in Gap stream located in Daejeon. In conclusion, the indicators are feasible and the selected indicators could be used to establish the direction and objectives of riparian restoration.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.177-187
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• 2005

The Land Use Suitability Assessment System was recently introduced by the Act on Planning and Utilization of the National Territory to use, manage, and develop the national territory, which integrated the National Land Use and Management Act and the Urban Planning Act. It provides a guideline for land use according to locational characteristics, usability, and developmental conditions of land in the vicinity. The database is constructed with LMIS cadastral data, posted land price data, and the data of related agencies such as the Korea Forest Service, the Ministry of Environment, and the Korea Water Resources Corporation etc. In this paper we describe accurate database construction method fur land use suitability assessment system as classifying and verifying errors deriving from database construction focused on Jinju city. Those data errors have the problems such as accuracy difference among the related agencies data, gap of data acquisition time, and non-consideration of latest updated data etc.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.83-101
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• 2022

Accurate atmospheric correction is essential for the analysis of land surface and environmental monitoring. Aerosol optical depth (AOD) information is particularly important in atmospheric correction because the radiation attenuation by Mie scattering makes the differences between the radiation calculated at the satellite sensor and the radiation measured at the land surface. Thus, it is necessary to use high-quality AOD data for an appropriate atmospheric correction of high-resolution satellite images. In this study, we examined the Second Simulation of a Satellite Signal in the Solar Spectrum (6S)-based atmospheric correction results for the Sentinel-2 images in South Korea using raster AOD (MODIS) and single-point AOD (AERONET). The 6S result was overall agreed with the Sentinel-2 level 2 data. Moreover, using raster AOD showed better performance than using single-point AOD. The atmospheric correction using the single-point AOD yielded some inappropriate values for forest and water pixels, where as the atmospheric correction using raster AOD produced stable and natural patterns in accordance with the land cover map. Also, the Sentinel-2 normalized difference vegetation index (NDVI) after the 6S correction had similar patterns to the up scaled drone NDVI, although Sentinel-2 NDVI had relatively low values. Also, the spatial distribution of both images seemed very similar for growing and harvest seasons. Future work will be necessary to make efforts for the gap-filling of AOD data and an accurate bi-directional reflectance distribution function (BRDF) model for high-resolution atmospheric correction. These methods can help improve the land surface monitoring using the future Compact Advanced Satellite 500 in South Korea.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.547-558
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• 2022

In August 2020, a debris flow occurred in Gokseon, Jeollanam-do, that resulted in the death of five residents. In this study area, high-resolution 0.03 m topographic information was generated through photogrammetry, and the amount of soil movement/loss was measured. In addition, sensitivity analysis was performed for flow depth, flow velocity, and debris flow area with the program Flo-2D using the difference in simulation parameter that discharge and topographic information. Wth increasing debris flow input discharge, increases were seen in flow depth, flow velocity, and debris flow area, as ell as in the gap in results from high-resolution topographic information and low-resolution topographic information. Also, when high-resolution topographic information was used, the results were similar to the actual (measured) flow direction of the debris flow. Therefore, the application of high-resolution topographic information increases the accuracy of the debris flow analysis results compared with low-resolution information. Results could be further imporved in the future by considering geological information such as yield stress and viscosity.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.3973-3982
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• 2023

As for two-phase flow in rectangular channels, the flow regimes especially like churn-turbulent and annular flow are significant for the physical problem like Countercurrent Flow Limitation (CCFL). In this study, the rectangular channels with cross-sections of 4 × 66 mm, 6 × 66 mm, 8 × 66 mm are adopted to investigate the flow regimes of air-water vertical upward two phase flow under adiabatic condition. The gas and liquid superficial velocities are 0 ≤ j_g ≤ 20m/s and 0.25 ≤ j_f ≤ 3m/s respectively which covering bubbly to annular flow. The flow regimes are identified by random forest algorithm and the flow regime maps are obtained. As the results, the transitional void fraction from slug to churn turbulent flow fluctuate from 0.47 to 0.58 which is significantly affected by the dimensional size of channel and flow rate. Besides, the void fraction at transitional points from churn-turbulent (slug) to annular flow are 0.66-0.67, which are independent with the gap size. Furthermore, a new criteria of slug to churn-turbulent flow is established in this study. In addition, by introducing the interfacial force model, the criteria of churn-turbulent (slug) flow to annular flow is verified.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Korean Journal of Ecology and Environment
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• v.43 no.3
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• pp.385-399
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• 2010

The objective of this study was to analyze long term temporal trends of water chemistry and spatial heterogeneity for 83 sampling sites of Geum-River watershed using water quality dataset during 2003~2007 (obtained from the Ministry of Environment, Korea). The water quality, based on multi-parameters of temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (TN), total phosphorus (TP), and electric conductivity (EC), largely varied depending on the landuse patterns, years and seasons. The watershed was classified into three different landuse types: forest stream (Fo), agricultural stream (Ag), and urban stream (Ur). Largest seasonal variabilities in most parameters occurred during the two months of July to August and these were closely associated with large spate of summer monsoon rain. Conductivity, used as a key indicator for an ionic dilution during rainy season, and nutrients of TN and TP had inverse functions of precipitation. BOD, COD decrease during the rainy season. Minimum values in the conductivity, TN, and TP were observed during the summer monsoon, indicating an ionic and nutrient dilution of river water by the rainwater. In contrast, major inputs of suspended solids (SS) occurred during the period of summer monsoon. The landuse patterns analyses, based on the variables of BOD, COD, TN, TP and SS, showed that the values were greater in the agricultural stream (Ag) than in the forest stream (Fo) and urban stream (Ur) and that water quality was worst in the urban stream (Ur). The overall dataset suggest that efficient water quality management, especially in Gap-Stream and Miho-Stream, which showed worst water quality is required along with some of urban stream (Ur), based on the analysis of landuse patterns.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.15-24
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• 2015

$CH_4$ is a trace gas and one of the key greenhouse gases, which requires continuous and systematic monitoring. The application of eddy covariance technique for $CH_4$ flux measurement requires a fast-response, laser-based spectroscopy. The eddy covariance measurements have been used to monitor $CO_2$ fluxes and their data processing procedures have been standardized and well documented. However, such processes for $CH_4$ fluxes are still lacking. In this note, we report the first measurement of $CH_4$ flux in a rice paddy by employing the eddy covariance technique with a recently commercialized wavelength modulation spectroscopy. $CH_4$ fluxes were measured for five consecutive days before and after the rice transplanting at the Gimje flux monitoring site in 2012. The commercially available $EddyPro^{TM}$ program was used to process these data, following the KoFlux protocol for data-processing. In this process, we quantified and documented the effects of three key corrections: (1) frequency response correction, (2) air density correction, and (3) spectroscopic correction. The effects of these corrections were different between daytime and nighttime, and their magnitudes were greater with larger $CH_4$ fluxes. Overall, the magnitude of $CH_4$ flux increased on average by 20-25% after the corrections. The National Center for AgroMeteorology (www.ncam.kr) will soon release an updated KoFlux program to public users, which includes the spectroscopic correction and the gap-filling of $CH_4$ flux.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.