• Korean Journal of Environmental Biology
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• v.26 no.3
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• pp.252-263
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• 2008

The purpose of this study was to determine trophic state, based on nutrients (TN, TP), transparency (SD), and chlorophyll-${\alpha}$ (Chl) and identify their empirical relations of TN-Chl, TP-Chl and Chl-SD depending on the dataset used along with dynamics of conductivity and suspended solids. Analysis of trophic states showed that more than half of 36 reservoirs were judged as eutrophic-hypertrophic conditions depending on the trophic variables. Seasonal values of TP varied by nearly 500% and showed greater in August than any other months. In contrast, TN varied within less than 90% and all monthly mean values of TN were never fall less than 1.2 mg L$^{-1}$ indicating low seasonal variations and high ambient concentrations (eutrophic-hypertrophic state). Analysis of empirical relations in the trophic variables showed that transparency had greater functional relations with Chl (R$^2$=0.31, p<0.001) than TP (R$^2$=0.15, p<0.001) and TN (R$^2$=0.20, p<0.001). Ratios of TN : TP in the ambient water indicated that most reservoirs showed a potential phosphorous limitation on the algal growth. Thus, algal biomass, based on Chl values, was more regulated by phosphorous than nitrogen. Analysis of linear regression model, based on log-transformed annual mean values, showed that only 30% in the variation of Chl was explained by TP (R$^2$=0.295, p=0.001, n=36) and 15% by TN (R$^2$=0.151, p=0.019, n=36). However, linear regression model, based on individual system, showed that Chl-TP model had strong positive relations (R$^2$=0.62, p=0.002, n=12), whereas the model had no any relations (p=0.892, n=12). Overall, our data suggested that averaging effect in the empirical model developments may influence the significance in the statistical analysis.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.515-530
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• 2021

This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R²) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R² was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R² was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R² of 0.69 and RMSE of 2.88%.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.3
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• pp.281-290
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• 2014

This study was conducted to evaluate the distribution characteristics of $PM_{10}$ and heavy metals concentrations in the ambient air of Gyeonggi-do area by region and season from February, 2013 to March, 2014. The regression model for the prediction of formation characteristics and contamination degree of $PM_{10}$ and heavy metals by correlation analysis and regression analysis for using the multivariate statistical analysis was also established. The main wind direction during the investigation period was South East (SE) and West South West (WSW) winds, and the concentration of $SO_2$ at Ansan with industrial region showed 1.6 times higher than Suwon, Euiwang with residential region. The concentrations (median) of Pb, Cu and Ni at Ansan showed 3.2~4.5, 1.9~2.2 and 1.7~2.6 times respectively higher than those at Suwon. By the seasonal concentration variation, the concentrations of $PM_{10}$, Pb, Fe and As in winter and spring (December to May) showed 1.7, 1.9, 1.9 and 2.7 times respectively higher than those in summer and fall (June to November). As, Fe and $PM_{10}$ had a big difference by the seasonal factors, and Cu and Ni were evaluated to be influenced by the regional factors. From the results of correlation analysis among the target items, the correlation coefficient of PM and Mn had 0.82 (p/0.01) and that of Fe and Mn had 0.82 (p/0.01), which showed high correlation. And the correlation coefficients for $SO_2$ and Pb, CO and $PM_{10}$ were 0.66 (p/0.01) and 0.62 (p/0.01) respectively. The multiple linear regression models for $PM_{10}$, Pb, Cu, Cr, As, Ni, Fe and Mn were established by independent variables of CO, $SO_2$ and meteorological factors (wind speed, relative humidity). In the regression models, independent variable $SO_2$ was in cause-and-effect relationship with all dependent variables, and $PM_{10}$, Fe and Mn were influenced by CO and wind speed, and Pb, Cu, Ni and As had a main factor of $SO_2$.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.52-61
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• 2006

The aim of this study was to analyze the seasonal patterns of zooplankton community dynamics in the lower Nakdong River (Mulgum, RK; river kilometer; 27 km from the estuarine barrage), with a Self-Organizing Map (SOM) based on weekly sampled data collected over ten years(1994 ${\sim}$ 2003). It is well known that zooplankton groups had important role in the food web of freshwater ecosystems, however, less attention has been paid to this group compared with other community constituents. A non-linear patterning algorithm of the SOM was applied to discover the relationship among river environments and zooplankton community dynamics. Limnological variables (water temperature, dissolved oxygen, pH , Secchi transparency, turbidity, chlorophyll a, discharge, etc.) were taken into account to implement patterning seasonal changes of zooplankton community structures (consisting of rotifers, cladocerans and copepods). The trained SOM model allocated zooplankton on the map plane with limnological parameters. Three zooplankton groups had high similarities to one another in their changing seasonal patterns, Among the limnological variables, water temporature was highly related to the zooplankton community dynamics (especially for cladocerans). The SOM model illustrated the suppression of zooplankton due to the increased river discharge, particularly in summer. Chlorophyll a concentrations were separated from zooplankton data set on the map plane, which would intimate the herbivorous activity of dominant grazers. This study introduces the zooplankton dynamics associated with limnological parameters using a nonlinear method, and the information will be useful for managing the river ecosystem, with respect to the food web interactions.

• Journal of the Korean earth science society
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• v.34 no.4
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• pp.336-344
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• 2013

This study has developed a multiple linear regression model (MLRM) for the seasonal prediction of the summer tropical cyclone genesis frequency (TCGF) over the western North Pacific (WNP) using the four teleconnection patterns. These patterns are representative of the Siberian high Oscillation (SHO) in the East Asian continent, the North Pacific Oscillation (NPO) in the North Pacific, Antarctic oscillation (AAO) near Australia, and the circulation in the equatorial central Pacific during the boreal spring (April-May). This statistical model is verified by analyzing the differences hindcasted for the high and low TCGF years. The high TCGF years are characterized by the following anomalous features: four anomalous teleconnection patterns such as anticyclonic circulation (positive SHO phase) in the East Asian continent, pressure pattern like north-high and south-low in the North Pacific, and cyclonic circulation (positive AAO phase) near Australia, and cyclonic circulation in the Nino3.4 region were strengthened during the period from boreal spring to boreal summer. Thus, anomalous trade winds in the tropical western Pacific (TWP) were weakened by anomalous cyclonic circulations that located in the subtropical western Pacific (SWP) in both hemispheres. Consequently, this spatial distribution of anomalous pressure pattern suppressed convection in the TWP, strengthened convection in the SWP instead.

• Journal of Ecology and Environment
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• v.37 no.2
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• pp.91-97
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• 2014

Little is known about how the increased porosity of a deciduous windbreak, which results from loss of leaves, influences wind speed reduction. We hypothesized that, with loss of foliage, the wind speed reduction effectiveness of a deciduous windbreak decreases on near leeward side but not on further leeward side and that wind speed recovers faster in the full foliage season than in other seasons. During summer, autumn, and winter (full, medium, and non-foliage season, respectively), we observed wind speed and direction around a deciduous windbreak in a traditional Korean village on windward and near and further leeward sides (at -8H, 2H, and 6H; H = 20 m, a windbreak height). We used a linear mixed effects model to determine that the relative wind speed reduction at 2H significantly decreased from 83% to 48% ($F_{2,111.97}=73.6$, P < 0.0001) with the loss of foliage. However, the relative wind speed reduction at 6H significantly increased from 26% to 43% ($F_{2,98.54}=18.5$, P < 0.0001). Consequently, wind speed recovery rate between 2H and 6H in summer was two times higher than in autumn and ten times higher than in winter ($F_{2,102.93}=223.1$, P < 0.0001). These results indicate that deciduous windbreaks with full foliage seem to induce large turbulence and increase wind speed recovery rate on leeward side. Our study suggests that further research is needed to find the optimal foliage density of a deciduous windbreak for maximizing windbreak effectiveness regardless of seasonal foliage changes.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.

• Korean Journal of Ecology and Environment
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• v.41 no.4
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• pp.457-465
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• 2008

The purpose of the study was to evaluate spatial and temporal dynamics of nutrients (TN, TP), organic pollution (BOD, COD), and ionic dynamics (electrical conductivity, EC) in the North Han-River, South Han-River, and merged downriver using the dataset of $1998{\sim}2007$, obtained from the MEK (Ministry of Environment, Korea). Accord. ing to interannual nutrient analysis, TN varied slightly in the North Han-River and South Han-River, but decreased in the merged downriver along with BOD. Longitudinal analysis in the water quality showed that BOD, COD, and nutrients had linear decreasing trend along the main axis of headwater-to-downriver. Concentrations of TP and TN in the North Han-River averaged $26.97{\mu}g\;L^{-1}$, $1.696mg\;L^{-1}$, respectively, which were minimum in the three watersheds, followed by South Han-River and then the merged downriver in order. Ratios of TN:TP in the watersheds were >40 in all the sites, indicating that nitrogen may be enough for periphyton or phytoplankton growth and phosphorus may be limited partially. After the North Han-River water is merged with South Han-River, the concentrations of BOD, COD, TN, and TP were similar to the values of $S6{\sim}S7$, respectively or a little bit higher, but increased abruptly in Site M4 (Fig. 3). Thus, mean values of all the water quality parameters in the reach of $M4{\sim}M7$ sites were greater than any other sites. Seasonal data analysis indicated that BOD and EC in the downstream ($S3{\sim}S7$) was greater in the premonsoon than two seasons of the monsoon and postmonsoon, and no significant differences in BOD between the three seasons were found in the upstream ($S1{\sim}S2$). Empirical models of COD in the merged downriver was predicted ($R^2=0.87$, p>0.01, slope = 0.84, intercept = -1.28) well by EC. These results suggest that EC to be measured easily in the field may be used for estimations of nutrients and organic matter pollutions in the merged downriver and these linear models are cost-effective for the monitoring of the parameters.