• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.169-176
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• 2012

This study describes the estimation of upstream ungauged watershed streamflow using downstream discharge data. For downstream Dongchon (DC) and upstream Kumho (KH) water level stations in Kumho river basin ($2,087.9km^2$), three methods of Soil and Water Assessment Tool (SWAT) modeling, drainage-area ratio method and regional regression equation were evaluated. The SWAT was calibrated at DC with the determination coefficient ($R^2$) of 0.70 and validated at KH with $R^2$ of 0.60. The drainage-area ratio method showed $R^2$ of 0.93. For the regional regression, the watershed area, average slope, and stream length were used as variables. Using the derived equation at DC, the KH could estimate the flow with maximum 41.2 % error for the observed streamflow.

• Journal of Environmental Science International
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• v.8 no.4
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• pp.431-441
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• 1999

In this study, the regional frequency analysis is used to determine each subbasin drought frequency with reliable monthly precipitation and the L-Moments method which is almost unbiased and has very nearly a normal distribution is used for the parameter estimation of monthly precipitation time series in Nakdong river basin. As the result of this study, the duration of '93-'94 is most severe drought year than any other water year and the drought frequency is established as compared the regional frequency analysis result of cumulative precipitation of 12th duration months in each subbasin with that of 12th duration months in the major drought duration. The Linear regression equation is induced according to linear regression analysis of drought frequency between Nakdong total basin and each subbasin of the same drought duration. Therefore, as the foundation of this study, it can be applied proposed method and procedure of this study to the water budget analysis considering safety standards for the design of impounding facilities large-scale river basin and for this purpose, above all, it is considered that expansion of reliable preciptation data is needed in watershed rainfall station.

• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.59-69
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• 1998

A regression model with basin physiographic characteristics as independent variables was calibrated for regional flood frequency analysis. In case that high correlations existing among the independent variables the ridge regression has been known to have capability of overcoming the problems of multicollinearity. To optimize the ridge regression model the cost function including regularization parameter must be minimized. In this research the genetic algorithm was applied on this optimization problem. The genetic algorithm is a stochastic search method that mimic the metaphor of natural biological heredity. Using this method the regression model could have optimized and stable weights of variables.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.407-416
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• 2016

The purpose of this study is to develop regional regression models to estimate mean low flow for ungauged basins. The unregulated streamflow data observed at 12 multipurpose dams and 4 irrigation dams were analyzed for determining mean low flows. Various types of regression models were developed using the relationship between mean low flows and various sets of watershed characteristics such as drainage area, average slope, drainage density, mean annual precipitation, runoff curve number. The performance of each regression model for estimating mean low flows was assessed by comparison with the results obtained from the observed data. It was found that a regional regression model explained by drainage area, the mean annual precipitation, and runoff curve number showed the best performance. The regression model presented in this study also gives better estimates of mean low flow than the estimates by the drainage-area ratio method and the previous regression model.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.325-340
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• 2008

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

• Journal of the Korean Regional Science Association
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• v.10 no.1
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• pp.1-18
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• 1994

The most important objective of the national development policy in Korea is the balanced regional development through the mitigation of concentration to the Capital region and the further development of other regions. Although various national policies have been formulated so far, the consequences of such policies for the balanced regional development have been unsatisfactory. This paper attempted to estimate regional growth factos through regression method. According to the results of this study, the differentiated regional policy for promotion and regulation to the location of firms and colleges and technological development have been operated only partially but not comprehenisively nor systematically. Especially, much of financial assistance has not been differentiated regionally. This study is expected to contribute to the formulation of the rational regional policy in future.

• Journal of the Korean Regional Science Association
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• v.33 no.2
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• pp.3-23
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• 2017

This study aims to understand how human capital is related to the inter-regional wage gap between the capital region and the non-capital region in Korea. We focus more specifically on whether the inter-regional wage gap is due to high levels of human capital in the capital region or due to high returns to human capital in the capital region. The decomposition method based on the unconditional quantile regression was used to examine how the relationship between human capital and the inter-regional wage gap varies along the wage distribution. When first estimating earnings functions from the two regions to apply this decomposition method, we included not only conventional indicators of human capital, such as education and on-the-job training, but also occupational skills including cognitive-interactive skills, technical skills, and physical skills. As a result, other things being equal, a large part of the inter-regional wage gap was explained by the human capital variables. Although the composition effect of the human capital variables existed in all the wage quantiles, the more important factor was the wage structure effect of the human capital variables. In addition, among the various human capital variables, the wage structure effect of years of education was a key factor in explaining the inter-regional wage gap. This study is meaningful in that it shows that the relationship between human capital and the inter-regional wage gap may vary depending on the wage quantiles.

• Korea Journal of Hospital Management
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• v.22 no.3
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• pp.46-60
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• 2017

The purpose of this study is to analyze the difference of productivity according to environmental factors among 25 Regional base public hospitals. Also this study is to propose a method to improve the productivity of Regional base public hospitals in the future by improving the public performance and stable management performance by studying the productivity variables affecting profitability. The survey period was based on the last three years, and 25 Regional base public hospitals were selected for the survey. The dependent variable is the total capital medical marginal profitability and the medical profit marginal profitability which are the indicators of profitability. The independent variable, productivity, is classified into three indicators: capital productivity, labor productivity, and value added productivity. The ANOVA analysis method was used to analyze the productivity difference according to the frequency factor and the environmental factors of the Regional base public hospitals. Finally, we conducted a hierarchical regression analysis to examine the productivity variables affecting profitability. The results of this study showed that there were differences in productivity due to environmental factors such as hospital size, competition in the local medical market, and differences in management performance. The difference in productivity and profitability depending on the environmental factors suggests that it is difficult for Regional base public hospitals in each regional base to perform a balanced public service. In order to overcome this, it is necessary to provide balanced medical services such as government financial support expansion, regional medical demand forecasting and facility infrastructure construction.