• Journal of Korea Water Resources Association
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• v.47 no.3
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• pp.269-283
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• 2014

This study evaluated the environmental impact based on watershed characteristics and climate change using RCP climate change scenarios provided by the Korea Meteorological Administration. Future dam inflow was estimated by the SWAT model. Dam safety evaluation and downstream duration curve analysis was performed using HEC-ResSim model. Trends of water quality was analyzed through seasonal-Kendall Test using existing water quality observation data. Release discharge and tributary runoff derived SWAT and HEC-ResSim models applied to Qual2E and the future change in water quality trends were analyzed. Integrated environmental review watershed following techniques will be able to obtain the river environment management system and environmental issues such as climate change, new guidelines for preemptively response will be provided.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• Journal of Korean Society on Water Environment
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• v.23 no.2
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• pp.260-265
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• 2007

This study was carried out to complement water quality standards and to establish new concept for water quality standards reflecting current state of water quality in small streams. By this purpose, discriminant analysis was performed and Water Quality Level Model (WQLM) was developed using the data such as EC, BOD, $COD_{Mn}$, SS, T-N, T-P, $NH_3-N$ in 224 agricultural streams. To give water quality level for water quality parameters, it divided into 20% respectively in the order of excellent water quality. On the basis of the lowest water quality level, water quality level of small streams is granted. As a result of it, number of stream corresponding to Level I was no, Level II was 2 streams, Level III was 22 streams, Level IV was 70 streams, and Level V was 130 streams. Average of water quality in each level was the highest in Level V. EC, SS, and T-N of 7 parameters were selected in variance concerned water quality level. By standardized canonical discriminant function coefficient, EC of three variances was the highest in 0.625 at the discriminant power. The next was T-N (0.509), SS (0.414). By discriminant function for water quality level, Level II was equal to $-2.973+19.376{\times}(EC)+0.647{\times}(T-N)+0.009{\times}(SS)$, Level III was equal to $-3.288+19.190{\times}(EC)+0.733{\times}(T-N)+0.041{\times}(SS)$, Level IV was equal to $-4.462+27.097{\times}(EC)+0.792{\times}(T-N)+0.053{\times}(SS)$, and Level V was equal to $-9.117+40.040{\times}(EC)+1.305{\times}(T-N)+0.111{\times}(SS)$. As a result of test at real agricultural watershed of Jeongan and Euidang in Gongju city, the fitness of WQLM was high to 88.78%. But, to get accomplished water quality assessment more exactly in agricultural streams, we had to concentrate and get vast data, and WQLM was modified and complemented continually.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.4
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• pp.517-532
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• 2021

Safe geological disposal of spent nuclear fuel (SNF) requires knowledge of the deep hydrochemical characteristics of the repository site. Here, we conducted a set of deep hydrochemical investigations using a 750-m borehole drilled in a model granite system in Wonju, South Korea. A closed investigation system consisting of a double-packer, Waterra pump, flow cell, and water-quality measurement unit was used for in situ water quality measurements and subsequent groundwater sampling. We managed the drilling water labeled with a fluorescein dye using a recycling system that reuses the water discharged from the borehole. We selected the test depths based on the dye concentrations, outflow water quality parameters, borehole logging, and visual inspection of the rock cores. The groundwater pumped up to the surface flowed into the flow cell, where the in situ water quality parameters were measured, and it was then collected for further laboratory measurements. Atmospheric contact was minimized during the entire process. Before hydrochemical measurements and sample collection, pumping was performed to purge the remnant drilling water. This study on a model borehole can serve as a reference for the future development of deep hydrochemical investigation procedures and techniques for siting processes of SNF repositories.

• Spatial Information Research
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• v.6 no.1
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• pp.91-102
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• 1998

The objectives of this research were to develop fish-habitat relationship models which can be used to estimate fish species riclmess of small and medium rivers in Korea, and test the accuracy of the models. The models are based on the Aquatic GAP Analysis model in the New York Cooperative Fish & Wildlife Research Unit (19%), and they employ three habitat factors; river size, physical habitat, and water quality of each river segment. Model 1 and model II are based on the water quality standard for life support of EP A and the water quality class of Korea, respectively. Test sites for this study include one urban stream and three less spoiled tributaries of the Han River. The results of this research can be summarized as follows. First, the number of habitat types identified by model I and model II are nine and 14, respectively. Second, the average accuracy of the three distribution maps of rare or endangered fish species are 80.6% (model 1) and 81.2% (model II). Third, the accuracy of fish species richness are 94% (model 1) and 95% (model II), and the water quality is the most important factor affecting fish species richness. Fourth, the accuracy of fish species list are 50.5% (model 1) and 68.7% (model II), but the accuracy of less spoiled stream segments and that of polluted stream segments are 67.1% and 86.5%, respectively. Finally, it can be concluded that the overall performance of model II is better than that of model I at our test sites.

• Environmental Engineering Research
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• v.21 no.2
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• pp.171-179
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• 2016

This study analyzes guidelines to select optimum number of grids to represent behavior of a given water system appropriately. The EFDC model was chosen as a 3-D hydrodynamic and water quality model and salt was chosen as a surrogate variable of pollutant. The model is applied to an artificial canal that receives salt water from coastal area and fresh water from a river from respective gate according to previously developed gate operation rule. Grids are subdivided in vertical and horizontal (longitudinal) directions, respectively until no significant changes are found in salinity concentrations. The optimum grid size was determined by comparing errors in average salt concentrations between a test grid systems against the most complicated grid system. MSE (mean squared error) and MAE (mean absolute error) are used to compare errors. The CFL (Courant-Friedrichs-Lewy) number was used to determine the optimum number of grid systems for the study site though it can be used when explicit numerical method is applied only. This study suggests errors seem acceptable when both MSE and MAE are less than unity approximately.

• Journal of Korean Society on Water Environment
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• v.26 no.6
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• pp.895-902
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• 2010

The effect of seasonality on water quality variation is very significant. Generally, it reduce the power of the trend extraction. A parametric time-series model was used for detecting trends in historic constituent concentration data. The effect of seasonality is able to remove from time series decomposition technique. According to such statistic methode, long-term water quality trend analysis system (NTrend 1.0) was developed by Nakdong River Water Environmental Research Center. The trend analysis of BOD variation was conducted with NTrend 1.0 at Goreong and Moolkum site in Nakdong river to show the effect of water quality management action plan. Power test of trend extraction was tried each case of 'deseasonalized and deannulized' data and 'deseasonalized' data. Analysis period was from 1989 to 2006, and it's period was divided again three times, 1989~1993, 1994~1999 and 2000~2006 according to action plan period. The BOD trend was downward in Goreong site during three times and it's trend slope was very steep, and upward in Moolkum during 1989~1993, but it was turned downward during 1994~1999 and 2000~2006. It was revealed that it's very effective to reduce the concentration of BOD by water quality management action plan in that watershed. The result of power test was shown that it is high for trend extraction power in case of 'deseasonalized' data.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.644-651
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on water quality such as turbidity, temperature, pH and alkalinity. However, the process of chemical reaction to improve water quality (e.g., turbidity) by chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts using Jar-test data. In this paper, a systematic control strategy is proposed to derive the optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride), using Jar-test results. A neural network model is developed for coagulant dosing and purifying process by means of six input variables (turbidity, temperature, pH, alkalinity of raw water, PAC feed rate, turbidity in flocculation) and one output variable, while considering the relationships to the reaction of coagulation and flocculation. The model is utilized to derive the optimum coagulant dosage (in the sense of minimizing turbidity of water in flocculator). The ability of the proposed control scheme validated through the field test has proved to be of considerable practical value.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.4
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• pp.469-479
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• 2015

This study have been conducted to analyze the feasibility of establishing Contamination Warning System(CWS) that is capable of monitoring early natural or intentional water quality accidents, and providing active and quick responses for domestic C_water supply system. In order to evaluate the water quality data set, pH, turbidity and free residual chlorine concentration data were collected and each statistical value(mean, variation, range) was calculated, then the seasonal variability of those were analyzed using the independent t-test. From the results of analyzing the distribution of outliers in the measurement data using a high-pass filter, it could be confirmed that a lot of lower outliers appeared due to data missing. In addition, linear filter model based on autoregressive model(AR(1) and AR(2)) was applied for the state estimation of each water quality data set. From the results of analyzing the variability of the autocorrelation coefficient structure according to the change of window size(6hours~48hours), at least the window size longer than 12hours should be necessary for estimating the state of water quality data satisfactorily.

• Journal of Korean Society on Water Environment
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• v.31 no.5
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• pp.507-522
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• 2015

In this study, some of the model verification results of STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model), a newly-developed hybrid watershed model, are presented for the runoff processes of nonpoint source pollution. For verification study of STREAM, the model was applied to a test watershed and a sensitivity analysis was also carried out for selected parameters. STREAM was applied to the Mankyung River Watershed to review the applicability of the model in the course of model calibration and validation against the stream flow discharge, suspended sediment discharge and some water quality items (TOC, TN, TP) measured at the watershed outlet. The model setup, simulation and data I/O modules worked as designed and both of the calibration and validation results showed good agreement between the simulated and the measured data sets: NSE over 0.7 and $R^2$ greater than 0.8. The simulation results also include the spatial distribution of runoff processes and watershed mass balance at the watershed scale. Additionally, the irrigation process of the model was examined in detail at reservoirs and paddy fields.