• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.39-50
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• 2006

The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.

• Journal of The Korean Society of Integrative Medicine
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• v.7 no.4
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• pp.119-130
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• 2019

Purpose : The purpose of this study was to investigate the meta-analysis on the effects of action observation training on stroke patients' walking. Methods : Domestic databases (DBpia, KISS, NDSL, and RISS) were searched for studies that conducted randomized controlled trials (RCTs) associated with action observation training in adults after stroke. The search outcomes were items associated with the walking function. The 18 studies that were included in the study were analyzed using R meta-analysis. A random-effect model was used for the analysis of the effect size because of the significant heterogeneity among the studies. Sub-group and meta-regression analysis were also used. Egger's regression test was conducted to analyze the publishing bias. Cumulative meta-analysis and sensitivity analysis were also done to analyze a data error. Results : The mean effect size was 2.77. The sub-group analysis showed a statistical difference in the number of training sessions per week. No statistically significant difference was found in the meta-regression analysis. Publishing bias was found in the data, but the results of the trim-and-fill method showed that such bias did not affect the obtained data. Also, the cumulative meta-analysis and sensitivity analysis showed no data errors. Conclusion : The meta-analysis of the studies that conducted randomized clinical trials revealed that action observation training effectively improved walking of the chronic stroke patients.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.5
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• pp.312-315
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• 2012

In this study, the model which can predict the quality of effluent has been implemented through multiple regression analysis to use operation data of a sewage treatment plant, to which a media process is applied. Multiple regression analysis were carried out by cases according to variable selection method, removal of outliers and log transformation of variables, with using data of one year of 2011. By reviewing the results of predictable models, the accuracy of prediction for $COD_{Mn}$ of treated water of secondary clarifiers was over 0.87 and for T-N was over 0.81. Using this model, it is expected to set the range of operating conditions that do not exceed the standards of effluent quality. In conclusion, the proper guidance on the effluent quality and energy costs within the operating range is expected to be provided to operators.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.3
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• pp.73-85
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• 2002

The purpose of this study was to develop prediction models for plant species abundance by stream restoration. Generally the stream plant is affected by stream gemophology. So in this study, the relationship between the vegetation abundance and stream gemophology was developed by multiple regression analysis. The stream characteristics utilized in this study were longitudinal slope, transectional slope, micro-landforms through the longitudinal direction, riparian width and geometric mean diameter and biggest diameter of bed material, and cumulated coarse and fine sand weight portion. The Pyungchang River with mountainous watershed and the Kyungan stream and the Bokha stream in the agricultural region were selected and vegetation species abundance and stream characteristics were documented from the site at 2~3km intervals from the upper stream to the lower. The Models for predicting the vegetation abundance were developed by multiple regression analysis using SPSS statistics package. The linear relationship between the dependant(species abundance) and independant(stream characteristics) variables was tested by a graphical method. Longitudinal and transectional slope had a nonlinear relationship with species abundance. In the next step, the independance between the independant variables was tested and the correlation between independant and dependant variables was tested by the Pearson bivariate correlation test. The selected independant variables were transectional slope, riparian width, and cumulated fine sand weight portion. From the multiple regression analysis, the $R^2$for the Pyungchang river, Kyungan stream, Bokga stream were 0.651, 0.512 and 0.240 respectively. The natural stream configuration in the Pyungchang river had the best result and the lower $R^2$for Kyunan and Bokha stream were due to human impact which disturbed the natural ecosystem. The lowest $R^2$for the Bokha stream was due to the shifting sandy bed. If the stream bed is fugitive, the prediction model may not be valid. Using the multiple regression models, the vegetation abundance could be predicted with stream characteristics such as, transection slope, riaparian width, cumulated fine sand weigth portion, after stream restoration.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.4
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• pp.13-30
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• 2020

This study aims to analyze the qualitative and quantitative effects of environmental policies in terms of green space management using logistic regression model(LRM). Landsat satellite imageries in 1985, 1992, 2000, 2008, and 2015 are classified using a hybrid-classification method. Based on these classified maps, logistic regression model having a deforestation tendency of the past is built. Binary green space change map is used for the dependent variable and four explanatory variables are used: distance from green space, distance from settlements, elevation, and slope. The green space map of 2008 and 2015 is predicted using the constructed model. The conservation effect of Ulsan's environmental policies is quantified through the numerical comparison of green area between the predicted and real data. Time-series analysis of green space showed that restoration and destruction of green space are highly related to human activities rather than natural land transition. The effect of green space management policy was spatially-explicit and brought a significant increase in green space. Furthermore, as a result of quantitative analysis, Ulsan's environmental policy had effects of conserving and restoring 111.75㎢ and 175.45㎢ respectively for the periods of eight and fifteen years. Among four variables, slope was the most determinant factor that accounts for the destruction of green space in the city. This study presents logistic regression model as a way of evaluating the effect of environmental policies that have been practiced in the city. It has its significance in that it allows us a comprehensive understanding of the effect by considering every direct and indirect effect from other domains, such as air and water, on green space. We conclude discussing practicability of implementing environmental policy in terms of green space management with the focus on a non-statutory plan.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.833-843
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• 2011

The purpose of this study is to analysis pollutant loading of upper Nakdong River basin according to the variation of discharge. The correlation between discharge and pollutant concentration and between discharge and pollutant loading were analyzed by statistical method, respectively. Regression equation of pollutant loading and discharge was represented as $L=_aQ^b$ in which L = pollutant loading(kg/day), and b = regression coefficients, and Q = discharge($m^3/day$). The correlation coefficient of study area was in range of 0.8428 to 0.9935. The SS was the highest b value 1.2856~1.7730 among water quality parameters because the pollutant loading of SS was much affected by flow. Additionally, the applicability of the regression equations was verified by comparing predicted results with observed value. The correlation coefficient of verification was in range of 0.8983 to 0.9987 and NSEC was in range of 0.7018 to 0.9960. Therefore the pollutant loading was good correlated with discharge. The main result will be used as basic data for water quality management and design of environment fundamental facilities.

• The Korean Journal of Emergency Medical Services
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• v.14 no.3
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• pp.5-11
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• 2010

Purpose: This study was to clarify correlation between en route distance and role time on call received hours. Methods: Data on en route distance(dependent variable), role time(independent variable) from 387 prehospital care reports documented by EMS in Kyonggi Provincial Fire and Disaster Headquarters and Seoul Metropolitan Fire and Disaster Department between 21 and 10 June 2010 were randomly chosen for simple regression analysis using Windows SPSS 12. OK. This analysis was conducted nine times on unit hour divided to eight call received and overall. Results: Statistically significant regression equations( Y=2.414+1.206X for 09:00~11:59, Y=3.753+.662X for 12:00~14:59, Y=2.215+1.458X for 15:00~17:59, Y=2.600+.822X for 21:00~23:59, Y=5. 445+.263X for overall) were derieved from the data. Conclusion: These equations having linear relationship may be utilized as a method for system status management to effectively response to emergency call.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.47-67
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• 2006

This study takes a quantitative approach to the influence of TTF (Task-Technology Fit) on the individual's use and performance of GSS (Group Support Systems), while traditional studies on TTF have taken the experimental approach to explore the characteristic fit between diverse tasks and technologies. We have the following two research inquires: Are the IS use and performance maximized when information technologies are provided by the exact amount of demand?; and, Does TTF at the high level between task and IT produce better IS use (or performance) than at the low level? To investigate these issues, we use the polynomial regression analysis and response surface methodology of Edwards (1993) instead of traditional direct measure of TTF. This method measures the degree of desired and actual level of information technologies in conducting tasks, and traces the dynamic changes of dependent variables (IS use and performance) according to the variances of each independent variable. Our results conclude that user's IS use and performance are maximized when information technologies are actually provided by no more or less than the desired level. We also found that TTF at the high level promotes better IS use and performance than TTF at the low level.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3967-3971
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• 2012

Aims: To explore the relationship between various molecular makers and liver metastasis of colorectal cancer (CRC). Method: Using immunohistochemistry, protein expression of CEA, nm23, c-met, MMP2, COX-2, VEGF, EGFR, and CD44 was assessed in 80 CRC cases. The Chi-square test and logistic regression were performed to analyze the relationship between these indicators and CRC liver metastasis. Results: There were significant differences in expression of CEA, MMP2, CD44, VEGF and EGFR between the liver metastasis and non metastasis groups (P < 0.05); no significant differences were noted for nm23, c-met, and COX-2 expression. Logistic regression analysis showed that only CEA, VEGF, and EGFR entered into the regression equation, and had significant correlations with CRC liver metastasis (${\alpha}$ inclusion= 0.10, ${\alpha}$ elimination = 0.15, R2 = 0.718). Conclusions: Combination detection of CEA, VEGF, and EGFR may be an effective means to predict CRC liver metastasis. Nm23, c-met, MMP2, COX-2, and CD44, in contrast, are not suitable as prognostic markers.

• Geomechanics and Engineering
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• v.9 no.1
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• pp.101-113
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• 2015

Saturated soil hydraulic conductivity is a very important soil parameter in numerous practical engineering applications, especially rainfall infiltration and slope stability problems. This parameter is difficult to measure since it is very highly sensitive to various soil conditions. There have been many analytical and empirical formulas to predict saturated soil hydraulic conductivity based on experimental data. However, there have been few studies to investigate in-situ hydraulic conductivity of weathered granite soils, which constitute the majority of soil slopes in Korea. This paper introduces an estimation method to derive saturated hydraulic conductivity of Korean weathered granite soils using in-situ experimental data which were obtained from a variety of slope areas of South Korea. A robust regression analysis was performed using different physical soil properties and an empirical solution with an $R^2$ value of 0.9193 was suggested. Besides that this research validated the proposed model by conducting in-situ saturated soil hydraulic conductivity tests in two slope areas.