• Journal of the Korean Geotechnical Society
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• v.17 no.6
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• pp.25-36
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• 2001

The purpose of this paper is to discuss the effects of soil properties such as liquid limit, water content, etc. on the compression index and to propose the empirical equation of compression index far regional clay and to verify the application Back Propagation Neural Network(BPNN). The compression index values obtained from laboratory tests are in the range of 0.01 to 3.06 for clay soils sampled in eleven regions. As the compare with the results of laboratory test and the predicted compression index value from the proposed empirical equations, the results of empirical equations including single soil parameter have a possibility to be overestimated. Also, the results of empirical equations including multiple soil parameters closed to the measured value more than that of empirical equations including single soil parameter, but the standard error for measured value obtained larger than 0.05. For these reasons, the empirical equations including single or multiple soil parameters proposed base on the results of laboratory test and the determination coefficient is up to 0.89. The result of BPNN shows that correlation coefficient and standard error between test and neural network result is larger than 0.925 and smaller than 0.0196, which means high correlativity, respectively. Especially, the estimated result by neural network, using only three parameters such as natural water content, dry unit weight and in-situ void ratio among various factors is available to the estimation of compression index and the correlation coefficient is 0.974. This result verified the possibility that if BPNN use, the compression index can be predicted by the parameters, which obtained from simplex field test.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.21-35
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• 2020

Vegetation information is a very important factor used in various fields such as urban planning, landscaping, water resources, and the environment. Vegetation varies according to canopy density or chlorophyll content, but vegetation vitality is not considered when classifying vegetation areas in previous studies. In this study, in order to satisfy various applied studies, a study was conducted to set a threshold value of vegetation index considering vegetation vitality. First, an eBee fixed-wing drone was equipped with a multi-spectral camera to construct optical and near-infrared orthomosaic images. Then, GIS calculation was performed for each orthomosaic image to calculate the NDVI, GNDVI, SAVI, and MSAVI vegetation index. In addition, the vegetation position of the target site was investigated through VRS survey, and the accuracy of each vegetation index was evaluated using vegetation vitality. As a result, the scenario in which the vegetation vitality point was selected as the vegetation area was higher in the classification accuracy of the vegetation index than the scenario in which the vegetation vitality point was slightly insufficient. In addition, the Kappa coefficient for each vegetation index calculated by overlapping with each site survey point was used to select the best threshold value of vegetation index for classifying vegetation by scenario. Therefore, the evaluation of vegetation index accuracy considering the vegetation vitality suggested in this study is expected to provide useful information for decision-making support in various business fields such as city planning in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.982-982
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• 2012

본 연구에서는 우리나라의 중 소규모 유역의 수문학적 위험도 분석을 위하여 한강유역을 대상으로 통합위험지수(IRI: Integrated Risk Index)를 산정하였으며, El Ni$\tilde{n}$o-Southern Oscillation (ENSO)에 의한 대규모 대기순환 패턴의 변화가 한강 유역의 통합위험지수 변화에 미치는 영향을 평가하였다. ENSO자료는 전통적인 El Ni$\tilde{n}$o에 해당하는 Cold-tongue (CT) El Ni$\tilde{n}$o와 중앙태평양 부근의 이상적인 해수면 온도 상승에 의한 Warm-pool (WP) El Ni$\tilde{n}$o, 그리고 해수면 온도가 이상적으로 낮게 관측되는 La Ni$\tilde{n}$a 기간으로 구분하였으며, 각 기간 중 가장 강한 ENSO가 발생한 해(CT El Ni$\tilde{n}$o, 1998; WP El Ni$\tilde{n}$o, 2005; La Ni$\tilde{n}$a, 2000)를 대상으로 통합위험지수를 산정하였다. 통합위험지수는 수문학적 요인(Hydrologic Components), 사회 경제적 요인(Socio-Economic Components)과 생태적 요인(Ecological Components)으로 구분하였고, 엔트로피(entropy) 기법을 통하여 각 인자와 요인별 가중치를 적용하였다. 중권역별 통합위험지수의 평가는 5개의 계급구간(Very High, High, Medium, Low, Very Low)으로 구분하였다. 분석결과, CT El Ni$\tilde{n}$o해의 유역평균 IRI 값은 0.58, WP El Ni$\tilde{n}$o해의 IRI 값은 0.57로 비슷한 결과를 보였으나, La Ni$\tilde{n}$a해에는 IRI 값이 0.41로 낮게 나타났다. CT와 WP El Ni$\tilde{n}$o해에는 한강 서쪽일부 중권역에서 통합위험지수가 높게 나타났으며, La Ni$\tilde{n}$a해에는 한강 중 동부 대부분 유역에서 낮게 분석되었다. 향후 유역별 통합위험지수 산정과 더불어 서로 다른 형태의 ENSO에 따른 수자원 변동 예측이 이루어진다면, 수자원의 효율적인 관리와 안정적인 용수공급에 도움을 줄 것으로 사료되며, 이는 유역별 수자원의 취약성 평가 및 위험도 분석을 위한 기초자료로 활용이 가능하리라 사료된다.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.395-432
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• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Korean Journal of Environmental Biology
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• v.36 no.4
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• pp.672-679
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• 2018

This study was conducted to predict the potential distribution and risk of invasive alien plant, Amaranthus patulus, in an agricultural area of South Korea. We collected 254 presence localities of A. patulus using field survey and literature search and stimulated the potential distribution area of A. patulus using maximum entropy modeling (MaxEnt) with six climatic variables. Two different kinds of agricultural risk index, raster risk index and regional risk index, were estimated. The 'raster risk index' was calculated by multiplying the potential distribution by the field area in $1{\times}1km$ and 'regional risk index' was calculated by multiplying the potential distribution by field area proportion in the total field of South Korea. The predicted potential distribution of A. patulus was almost matched with actual presence data. The annual mean temperature had the highest contribution for distribution modeling of A. patulus. Area under curve (AUC) value of the model was 0.711. The highest regions were Gwangju for potential distribution, Jeju for 'raster risk index' and Gyeongbuk for 'regional risk index'. This different ranks among the index showed the importance about the development of various risk index for evaluating invasive plant risk.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.83-88
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• 2013

Foreign matters in foods are important food safety issue of consumers, retailers, food manufacturers and food safety authorities in Korea. In order to provide information for development of risk management options and detection technology for foreign matters, multifactorial risk of foreign matters in foods was estimated based on various factors including detection rate, health adverse effect, economic and social aspects. For the each of five food items and foreign matters which were selected from previous study, factors including detection rates, health adverse effects, annual production amounts and willingness to additional pay to reduce foreign matters in foods were quantitatively estimated. The highest risk score was estimated for metal-noodle combination followed by insect-noodle and metal-beverage combinations. The multifactorial risk assessment on foreign matters in food could provide useful information to support risk managers and scientist in complex decision making when various factors should be concerned and different food-foreign matter combinations are compared.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.465-480
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• 2009

In this study, single and multiple linear regression model were used to derive the relationship between precipitation and altitude, latitude and longitude in Jejudo. The single linear regression analysis was focused on whether orographic effect was existed in Jejudo by annual average precipitation, and the multiple linear regression analysis on whether orographic effect was applied to each duration and return period of quantile from regional frequency analysis by index flood method. As results of the regression analysis, it shows the relationship between altitude and precipitation strongly form a linear relationship as the length of duration and return period increase. The multiple linear regression precipitation estimates(which used altitude, latitude, and longitude information) were found to be more reasonable than estimates obtained using altitude only or altitude-latitude and altitude-longitude. Especially, as results of spatial distribution analysis by kriging method using GIS, it also provides realistic estimates for precipitation that the precipitation was occurred the southeast region as real climate of Jejudo. However, the accuracy of regression model was decrease which derived a short duration of precipitation or estimated high region precipitation even had long duration. Consequently the other factor caused orographic effect would be needed to estimate precipitation to improve accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.19-27
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• 2017

This research was aimed to analyze landslide susceptibility and compare the prediction accuracy using ensemble frequency ratio (FR) and logistic regression at the Inje area, Korea. The landslide locations were identified with the before and after aerial photographs of landslide occurrence that were randomly selected for training (70%) and validation (30%). The total twelve landslide-related factors were elevation, slope, aspect, distance to drainage, topographic wetness index, stream power index, soil texture, soil sickness, timber age, timber diameter, timber density, and timber type. The spatial relationship between landslide occurrence and landslide-related factors was analyzed using FR and ensemble model. The produced LSI maps were validated and compared using relative operating characteristics (ROC) curve. The prediction accuracy of produced ensemble LSI map was about 2% higher than FR LSI map. The LSI map produced in this research could be used to establish land use planning and mitigate the damages caused by disaster.

• Journal of Chest Surgery
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• v.39 no.10 s.267
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• pp.749-753
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• 2006

Background: The purpose of this study is to assess a score system for operative risk evaluation of CABG. Material and Method: From January 2001 to September 2005, retrospective study for various perioperative factors of 2993 cases was done. Result: The early operative mortality was 2.4% and the beta coefficients of 7 core variables related to it (preoperative LV dysfuction, preoperative renal failure, MI within 1 week, reoperation, combined surgery, preoperative atrial fibrillation, preoperative IABP) were adjusted to score system. ROC curve and Hosmer and Lemeshow goodness of fit test was done. Conclusion: This score system was effective in assessing operative risk of CABG. But It is necessary to gather larger volume of case and perform multicenter study.

• Journal of Korean Society of Forest Science
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• v.88 no.3
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• pp.379-388
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• 1999

As a first step for delineating the provenance regions of the forest trees in Korea, horizontal zones have been deduced primarily from the various climatic factors such as annual mean temperature, extremely low temperature, relative humidity, annual gum of possible growing days, duration of sunshine and dry index. The basic concept to the delineation of the provenance regions was based on the ecological regions, which was likely to be more practical than that on the basis of the typical provenance regions at the species level. Primary classification of the regions has been based on the forest zones(sub-tropical, warm-temperate, mid-temperate and cool-temperate) as a broad geographic region. Further classification has been carried out using cluster analyses among the basic regions within forest zone. On the basis of clustering, a total of 19 regions including 3 from sub-tropical, 6 from warm-temperate, 8 from mid-temperate and 2 from cool-temperate was horizontally delineated. Of the mean values of 6 climate factors at the broad geographic region level, three factors such as annual mean temperature, extremely low temperature, annual growing days showed directional tendencies from subtropical to cool-temperate, while the others didn't. The values of relative humidity, duration of sunshine and dry index varied among the provenance regions within forest zone. These three factors might he more sensitive by the micro-environment condition than by the macro-environment condition. Present study aimed to delineate the primary provenance regions for tentative application to forest practices. These will be stepwise revised through the supplement using accumulated information regard to genecological data.