• Korean Journal of Ecology and Environment
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• v.40 no.1
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• pp.14-30
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• 2007

The purpose of this study was to evaluate trophic conditions of various Korean reservoirs using Trophic State Index (TSI) and predict the reservoir conditions by empirical models. The water quality dataset (2000, 2001) used here were obtained from the Ministry of Environment, Korea. The water quality, based on multi-parameters of dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), suspended solid (SS), Secchi depth (SD), chlorophyll-${\alpha}$ (CHL), and conductivity largely varied depending on the sampling watersheds and seasons. In general, trophic conditions declined along the longitudinal axis of headwater-to-the dam and the largest seasonal variations occurred during the summer monsoon of July-August. Major inputs of TP occurred during the monsoon (r=0.656, p=0.002) and this pattern was similar to solid dynamics of SS (r=0.678, p<0.001). Trophic parameters including CHL, TP, SD, and TN were employed to evaluate how the water systems varies with season. Trophic State Index (TSI, Carlson, 1977), based on TSI (CHL), TSI (TP), and TSI (SD), ranged from mesotrophic to eutrophic. However, the trophic state, based on TSI (TN), indicated eutrophic-hypereutrophic conditions in the entire reservoirs, regardless of the seasons, indicating a N-rich system. Overall, nutrient data showed that phosphorus was a primary factor regulating the trophic state. The relationships between CHL (eutrophication index) vs. trophic parameters (TN, TP, and SD) were analysed to develop empirical models which can predict the trophic status. Regression analyses of log-transformed seasonal CHL against TP showed that the value of $R^2$ was 0.31 (p=0.017) in the premonsoon but was 0.69 (p<0.001) during the postmonsoon, indicating a greater algal response to the phosphorus during the postmonsoon. In contrast, SD had reverse relation with TP, CHL during all season. TN had weak relations with CHL during all seasons. Overall, data suggest that TP seems to be a good predictor for algal biomass, estimated by CHL, as shown in the empirical models.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• 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.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Journal of the Korea Institute of Building Construction
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• v.16 no.4
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• pp.349-357
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• 2016

Recently, the quantity of risk in construction project has been inflated due to the fact that current construction projects have been large and complicated. Therefore, a study on the risk management methods is necessary that can predict and respond to the need in complicated modern construction projects. In this study, the objective is to analyze the cause of accident in actual construction sites and develop a risk assessment model based on insurance claims records. To reach the goal of this study, first, the frequency and severity of accidents are analyzed the causes of accidents based on the classification; progress rate, season, and total construction costs. Second, a risk assessment model is developed by utilizing a multiple regression analysis. The dependent variable is loss ratio of material damage and three categories; natural hazards, geographic information, and construction method & ability, are used as the independent variables. The model's adjusted R-square is 0.455. The contributions of this study will be used as a material for a quantitative risk analysis model development and review of the construction risk factors for future study.

• Korean Journal of Ecology and Environment
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• v.35 no.2 s.98
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• pp.79-91
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• 2002

A time variable modeling study was performed for seasonal variations of vertical temperature profiles in the Okjung Lake located in upstream of the Sumjin River. Based on the model structure of the US Army Corps of Engineer's CE-QUAL-W2, the lake was divided into 3 branches, 50 longitudinal segments and 49 vertical layers and vertical profiles of water temperature and current velocity were simulated over one year. The model results were calibrated and verified against vertical profiles of water temperature measured every month from March 1998 to February 1999 at 5 different locations. The model results showed a good agreement with the field measurements. The hydrologic balance during this period was validated by comparing the simulated values of surface elevation level with the measured data. There was some discrepancy in July data between the model results and the fleld measurements. This could be attributed partially to the inadequacy of the model to the highly hydrodynamic nature of water body and partially to the lack of accuracy in local atmospheric temperature data during summer monsoon period. The model results have shown that there was no seasonal over-turn in most part of the Okjung Lake, where water temperature maintained above $4^{\circ}C$ over one year. In the upstream shal-low area (depth<20 meter), however, temperature at surface layer fell below $4^{\circ}C$ and water was frozen such that slight over-turn would occur during winter period. From this study, we concluded that the Okjung Lake is oligomictic. This conclusionis significantly different from the general pattern that the lakes located from $20^{\circ}C$ to $40^{\circ}C$ latitude would be warm monomictic. From the examination of simulated current velocity distribution, it was found that the upstream inflows would infiltrate into mesolimnion of the lake during hydrodynamic summer monsoon periods due to the thermal density of water.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.333-343
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• 2015

This research aims at identifying the goshawk's possible and replaceable breeding ground by using the MaxEnt prediction model which has so far been insufficiently used in Korea, and providing evidence to expand possible protection areas for the goshawk's breeding for the future. The field research identified 10 goshawk's nests, and 23 appearance points confirmed during the 3rd round of environmental research were used for analysis. 4 geomorphic, 3 environmental, 7 distance, and 9 weather factors were used as model variables. The final environmental variables were selected through non-parametric verification between appearance and non-appearance coordinates identified by random sampling. The final predictive model (MaxEnt) was structured using 10 factors related to breeding ground and 7 factors related to appearance area selected by statistics verification. According to the results of the study, the factor that affected breeding point structure model the most was temperature seasonality, followed by distance from mixforest, density-class on the forest map and relief energy. The factor that affected appearance point structure model the most was temperature seasonality, followed by distance from rivers and ponds, distance from agricultural land and gradient. The nature of the goshawk's breeding environment and habit to breed inside forests were reflected in this modeling that targets breeding points. The northern central area which is about $189.5 km^2$(2.55 %) is expected to be suitable breeding ground. Large cities such as Cheongju and Chungju are located in the southern part of Chungcheongbuk-do whereas the northern part of Chungcheongbuk-do has evenly distributed forests and farmlands, which helps goshawks have a scope of influence and food source to breed. Appearance point modeling predicted an area of $3,071 km^2$(41.38 %) showing a wider ranging habitat than that of the breeding point modeling due to some limitations such as limited moving observation and non-consideration of seasonal changes. When targeting the breeding points, a specific predictive area can be deduced but it is difficult to check the points of nests and it is impossible to reflect the goshawk's behavioral area. On the other hand, when targeting appearance points, a wider ranging area can be covered but it is less accurate compared to predictive breeding point since simple movements and constant use status are not reflected. However, with these results, the goshawk's habitat can be predicted with reasonable accuracy. In particular, it is necessary to apply precise predictive breeding area data based on habitat modeling results when enforcing an environmental evaluation or establishing a development plan.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.12
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• pp.104-110
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• 2010

Japan TV broadcasting signals are measured and analyzed to estimate spillover level from Japan during 44 months from Feb. 2003 to Oct. 2009. Electric field intensity spillover into Korea is measured using fixed measurement system in Busan, and the relationship between the measured data and the predicted value is analyzed by year and seasons. Measurement results show that spillover signals from Japan have a serious effect on Korea broadcasting environment, and so that proper digital switchover strategy is necessary to cope with the spillover situation.