• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.269-270
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• 2009

This study builds a sediment rating curve using the measured sediment yield and the simulated soil erosion by a GIS-embedded empirical model. Then the structured sediment rating curve is used to determine the SDR on a basin scale in southern Korea. The whole data(year of 2002-2008) are divided into two groups and the first group(year of 2002-2005) is used for calibration, while the other is used for validation. Two cases(rainfall amount and rainfall intensity) are analyzed to consider the rainfall runoff erosivity factor in simulating soil erosion. The results show the derived SDR provides reasonable accuracy and rainfall intensity gives better performance in calculating soil erosion than rainfall amount.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.4
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• pp.213-220
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• 2005

The Youngwal 1, Youngwal 2 and Youngchun gaging stations are observed flood flow and low flow during Mar. 2004~Oct. 2004. They are observed water stages and flow velocities for flood and low flow. The observed data are used to derived rating curve and equations. The HEC-RAS model is applied for hydraulic modeling in gauging stations. The model is designed to perform one-dimensional hydraulic calculations for an river improvement plan in a full network of natural and constructed channels, and is comprised of a graphical user interface(GUI), separate hydraulic analysis components, data storage and management capabilities, graphics and reporting facilities.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.1
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• pp.27-38
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• 2006

In this study, we developed a program to estimate discharge efficiently considering major hydraulic characteristic including water level, river bed, water slope and roughness coefficient in a natural river. Stream discharge was measured at Gongju gauge station located in the down stream of the Daechung Dam during normal and dry seasons from 2003 to 2004. The developed model was compared with the results from the existing rating curve at T/M gage stations, and was used for runoff analyses. Evaluating the developed river discharge estimation program, it was applied during 1983-2004 that base flow separation method and RRFS (Rainfall Runoff Forecasting System) which is based on SSARR (Streamflow Synthesis And Resevoir Regulation). The result presents the stage-discharge curve creator range at the Gong-ju is overestimated by approximately $10-20\%$, especially at the low stage. It is attributed to the hydraulic characteristics at the study. The discharge simulated by the RRFS and base flow separation, which is calibrated using the measurement at the early spring and late fall season during relatively d]v season, shows the least errors. The coefficient of roughness at Gongju station varied with the high and low water level.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.349-350
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• 2005

• Journal of Korean Society on Water Environment
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• v.29 no.4
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• pp.523-530
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• 2013

This study has been carried out to clarify the characteristics of discharge and pollutant loading according to flow conditions at jiseok stream watershed (JSW). A flow rate and pollutant load in the study watershed were estimated by equation of stage-discharge and discharge-loads rating curve. By using the methods above, I've evaluated the water quality (WQ) of the JSW if it is satisfied with the standard target. I've collected the data of BOD and T-P from the JSW every 8 days for the duration of 12 months. And then, I've schematized the data upon the load duration curve and the results showed me that the WQ of JSW was satisfied with the standard target. I've also collected the same data every each day for the duration of 12 months from JSW and have schematized the data again. And the results showed that it also was satisfied with the standard target. To be concluded, I've determined that point pollution sources of JSW gives more significant impacts to the WQ than non-point pollution sources of JSW and hence, as time goes, point pollution sources will keep depriciating the WQ of JSW. Therefore, further efforts will be required to JSW to maintain the WQ.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1025-1033
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• 2003

In common with workers in hydrologic fields, scientists and engineers relate one variable to two or more other variables for purposes of predication, optimization, and control. Statistics methods have improved to establish such relationships. Regression, as it is called, is indeed the most commonly used statistics technique in hydrologic fields; relationship between the monitored variable stage and the corresponding discharges(rating curve). Regression methods expressed in the form of mathematical equations which has parameters, so called parametric methods. some times, the establishment of parameters is complicated and uncertain. Many non-parametric regression methods which have not parameters, have been proposed and studied. The most popular of these are kernel regression method. Kernel regression offer a way of estimation the regression function without the specification of a parametric model. This paper conducted comparisons of some bandwidth selection methods which are using the least squares and cross-validation.

• 전자공학회논문지 IE
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• v.46 no.3
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• pp.62-67
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• 2009

In this paper, ROC curves were designed by using Fuzzy Logic Systems. ROC curve is used for diagnostic evaluation and the person evaluating ROC curve is chosen as a first-level diagnostician. For rating diagnostic capability on ROC curve through learning, the chest X-ray image is used. The images used for making a diagnosis are X-ray film being both noise and signal. The result over diagnostic capability difference between the male and the female represented a man had better than a woman but that difference can be ignored.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.943-958
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• 2008

This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1247-1248
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• 2005

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.661-671
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• 2017

The objective of this study was to analyze the flood stage considering the uncertainty caused by the river roughness coefficients and discharge. The methodology of this study involved the GLUE (Generalized Likelihood Uncertainty Estimation) to quantify the uncertainty bounds applying three different storm events. The uncertainty range of the roughness was 0.025~0.040. In case of discharge, the uncertainty stemmed from parameters in stage-discharge rating curve, if h represents stage for discharge Q, which can be written as $Q=A(h-B)^C$. Parameters in rating curve (A, B and C) were estimated by non-linear regression model and assumed by t distribution. The range of parameters in rating curve was 5.138~18.442 for A, -0.524~0.104 for B and 2.427~2.924 for C. By sampling 10,000 parameter sets, Monte Carlo simulations were performed. The simulated stage value was represented by 95% confidence interval. In storm event 1~3, the average bound was 0.39 m, 0.83 m and 0.96 m, respectively. The peak bound was 0.52 m, 1.36 m and 1.75 m, respectively. The recurrence year of each storm event applying the frequency analysis was 1-year, 10-year and 25-year, respectively.