• Journal of Radiation Protection and Research
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• v.37 no.3
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• pp.108-115
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• 2012

The Urban Areas Working Group within the EMRAS-2 ($\underline{E}$nvironmental $\underline{M}$odelling for $\underline{RA}$diation $\underline{S}$afety, Phase 2), which has been supported by the IAEA (International Atomic Energy Agency), has designed some types of accidental scenarios to test and improve the capabilities of models used for evaluation of radioactive contamination in urban areas. For the comparison of the results predicted from the different models, the absorbed doses in air were analyzed as a function of time following the accident with consideration of countermeasures to be taken. Two kinds of considerations were performed to find the dependency of the predicted results. One is the 'accidental season', i.e. summer and winter, in which an event of radioactive contamination takes place in a specified urban area. Likewise, the 'rainfall intensity' on the day of an event was also considered with the option of 1) no rain, 2) light rain, and 3) heavy rain. The results predicted using a domestic model of METRO-K have been submitted to the Urban Areas Working Group for the intercomparison with those of other models. In this study, as a part of these results using METRO-K, the countermeasures effectiveness in terms of dose reduction was analyzed and presented for the ground floor of a 24-story business building in a specified urban area. As a result, it was found that the countermeasures effectiveness is distinctly dependent on the rainfall intensity on the day of an event, and season when an event takes place. It is related to the different deposition amount of the radionuclides to the surfaces and different behavior on the surfaces following a deposition, and different effectiveness from countermeasures. In conclusion, a selection of appropriate countermeasures with consideration of various environmental conditions may be important to minimize and optimize the socio-economic costs as well as radiation-induced health detriments.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.11-18
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• 2018

The climate change impacts on hydrological components and water balance in Jeju Island were evaluated using multiple climate models and watershed model, SWAT-K. To take into account the uncertainty of the future forecast data according to climate models, climate data of 9 GCMs were utilized as weather data of SWAT-K for future period (2010-2099). Using the modeling results of the past (1992-2013) and the future period, the hydrological changes of each year were analyzed and the precipitation, runoff, evapotranspiration and recharge were increasing. Compared with the past, the change in the runoff was the largest (up to 50% increase) and the evapotranspiration was relatively small (up to 11% increase). Monthly results show that the amount of evapotranspiration and the amount of recharge are greatly increased as the amount of precipitation increases in August and September, while the amount of evapotranspiration decreases in the same period. January and December showed the opposite tendency. As a result of analyzing future water balance changes, the ratio of runoff, evapotranspiration, and recharge to rainfall did not change much, but compared to the past, the runoff rate increased up to 4.3% in the RCP 8.5 scenario, while the evapotranspiration rate decreased by up to 3.5%. Based on the results of other researchers and this study, it is expected that rainfall and runoff will increase gradually in the future under the assumption of present climate change scenarios. Especially summer precipitation and runoff are expected to increase. As a result, the amount of groundwater recharge in Jeju Island will increase.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.2
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• pp.127-139
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• 2024

Climate change has increased extreme weather events likewise heatwaves, heavy rain, and drought. Unlike other natural disaster, drought is a slowly developing phenomenon and thus drought damage increases as the drought continues. Therefore, it is necessary to understand the characteristics and mechanism of drought occurrence. Agricultural drought occurs when the water supply needed by crops becomes insufficient due to lack of soil water. Therefore, soil water is used as a key variable affecting agricultural drought. In this study, we examined the spatio-temporal distribution and trends of drought across the Korean Peninsula by determining the soil available water content (SAWC) through a model that integrated soil, meteorological, and crop data. Moreover, an investigation into the correlation between meteorological variables and the SAWC was conducted to assess how meteorological characteristics influence the nature of drought occurrences. During the soybean cultivation period, the average SAWC was lowest in 2018 at 88.6% and highest in 2021 at 103.2%. Analysis of the spatial distribution of SAWC by growth stage revealed that the lowest SAWC occurred during the flowering stage (S3) in 2018, during the leaf extension stage (S2) in 2019, during the seedling stage (S1) in 2020, again during the flowering stage (S3) in 2021, and during the seedling stage (S1) in 2022. Based on the average SAWC across different growth stages, the frequency of upland drought was the highest at 22 times during the S3 in 2018. The lowest SAWC was primarily influenced by a significant negative correlation with rainfall and evapotranspiration, whereas the highest SAWC showed a significant positive correlation with rainfall and relative humidity, and a significant negative correlation with reference evapotranspiration.

• Applied Biological Chemistry
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• v.44 no.2
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• pp.97-102
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• 2001

Cheju island depends on a hydrogeologically vulnerable aquifer system as its principle source of drinking water. Most of golf courses are located in the area which is important for the ground water recharge, and pesticides are applied to golf courses often at relatively high rates. Therefore, turf pesticides in golf course should be applied without adversely impacting ground water. In this experiment, downward movement of pesticides was monitored in model greens of golf course, where different adsorbents were layered in 3-cm thickness at 35-cm depth, and effect of the adsorption layer on the leaching loss of pesticides was investigated. Major leachings were observed in the periods of heavy rain and very limited leaching was observed under artificial irrigation. Fenitrothion and triadimefon, which have relatively short persistence and high adsorption coefficient, were found in the leachate in low concentrations only at the first rainfall event, around 20 days after the pesticide application. However, diniconazole, which has a relatively long half-life (97 days), was detected in the leachate during the whole period of experiment and concentration was much higher than those of the other pesticides. Maximum leachate concentrations were 1.9, 10.3, and 84.5 ${\mu}l^{-1}$ for fenitrothion, triadimefon, and diniconazole, respectively. Therefore, in golf course green which allows rapid water percolation and has extremely low adsorption capacity, persistence in soil could be more important factor in determination of leaching potential of pesticides. Total quantity of pesticides leached from the model green was <0.2% for fenitrothion and triadimefon and 1.8% for diniconazole. Adsorption layers significantly reduced pesticide leaching, and active carbon and Orpar were more effective than zeolite. In the model green having adsorption layer of active carbon or Orpar, leaching loss of pesticides was reduced below 0.01% of the initial application.

• Journal of Environmental Science International
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• v.16 no.4
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• pp.523-532
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• 2007

This study utilized the 1/25,000 topographic map of the upper area from the Geum-ho watermark located at the middle of Geum-ho river from the National Geographic Information Institute. For the analysis, first, the influence of the size of critical area to the hydro topographic factors was examined changing grid size to $10m{\times}10m,\;30m{\times}30m\;and\;50m{\times}50m$, and the critical area for the formation of a river to $0.01km^2{\sim}0.50km^2$. It is known from the examination result of watershed morphology according to the grid size that the smaller grid size, the better resolution and accuracy. And it is found, from the analysis result of the degree of the river according to the minimum critical area for each grid size, that the grid size does not affect on the degree of the river, and the number of rivers with 2nd and higher degree does not show remarkable difference while there is big difference in the number of 1st degree rivers. From the results above, it is thought that the critical area of $0.15km^2{\sim}0.20km^2$ is appropriate for formation of a river being irrelevant to the grid size in extraction of hydro topographic parameters that are used in the runoff analysis model using topographic maps. Therefore, the GIUH model applied analysis results by use of the river level difference law proposed in this study for the explanation on the outflow response-changing characters according to the decision of a critical value of a minimum level difference river, showed that, since an ogival occurrence time and an ogival flow volume are very significant in a flood occurrence in case of not undertow facilities, the researcher could obtain a good result for the forecast of river outflow when considering a convenient application of the model and an easy acquisition of data, so it's judged that this model is proper as an algorism for the decision of a critical value of a river basin.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

• Geotechnical Engineering
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• v.2 no.1
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• pp.27-44
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• 1986

The measured soil data are analyzed to the descriptive statistics and classified into the four models of uncorrelated-normal (UNNO), uncorrelated-nonnormal (VNNN), correlatedonnormal(CONN), and correlated-nonnormal(CONN) . This paper presents the comparisons of reliability index and check points using the advanced first-order second-moment method with respect to the four models as well as BASIC Program. A sin91e-mode Performance function is consisted of the basic design variables of bearing capacity and settlements on shallow foundations and input the above analyzed soil informations. The main conclusions obtained in this study are summarized as follows: 1. In the bearing capacity mode, cohesion and bearing-capacity factors by C-U test are accepted for normal and lognormal distribution, respectively, and negatively low correlated to each other. Since the reliability index of the CONN model is the lowest one of the four model, which could be recommended a reliability.based design, whereas the other model might overestimate the geotechnical conditions. 2. In the case of settlements mode, the virgin compression ratio and preccnsolidation pressure are fitted for normal and lognormal distribution, respectively. Constraining settlements to the lower ones computed by deterministic method, The CONN model is the lowest reliability of the four models.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.

• Journal of the Korean Society of Costume
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• v.64 no.2
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• pp.35-49
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• 2014

In cases of fashion businesses operating by consignment, base estimate on quantity of sales is the most essential part of merchandising. This study classified factors influential to sales into factors with systematic influence and factors with unsystematic influence. In order to find out influence of each factor on sales, non-linear regression was used with SPSS package on the basis of actual data on sales for 5 years for sport shoes brand. Major findings of this study are as follows. First, price level had significant negative(-) influence on sales. Second, price expectation effects had significant negative(-) influence on sales. Third, competitor's price effect showed significant negative(-) value. Fourth, day-of-the-week effect showed significant positive(+) effect. The theoretical marketing implications of this study are as follows. First, study on price leads to expansion of the researches from apparels to sport shoes. Field of study on price was enlarged through expansion of variable of study from price level and price expectation effect to promotion, day-of-the-week effect and rainfall effect. Second, quantitative scale of day-of-the-week effect was found and it could be confirmed that there was seasonal differences with day-of-the-week effect. Implications of above findings on marketing managers are as follows. First, it was found that an increase in competitiveness of brand power and a decline in absolute value of competitor's price effect can be realized when new product groups are developed to meet the unsatisfied needs in the market. Second, it was possible to find out the parameters scales of the price response function, making it possible to estimate sales for the next season, and in turn realize increase in rate of sales and profit rate. This research is based on the dynamic price response function, which is rare to find in the apparel business and it academic significance due to its expanding response model which was focused on price in conventional researches to non-systematic variables.