• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1047-1058
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• 2003

Seasonal occurrence of high ammonia nitrogen(NH3-N) concentrations has hampered chemical treatment processes of a water plant that intakes water at Buyeo site of Geum river. Thus it is often needed to quantify the effect of Daecheong Dam ouflow on the mitigation of $NH_3$-N contamination. In this study, multiple regression models were developed for forecasting daily $NH_3$-N concentrations using 8 years of water quality and dam outflow data, and verified with another 2 years of data set. During model development, the coefficients of determination($R^2$) and model efficiency($E_{m}$) were greater than 0.95. The verification results were also satisfactory although those statistical indices were slightly reduced to 0.84∼0.94 and 0.77∼0.93, respectively. The validated model was applied to assess the effect of different amounts of dam outflow on the reduction of $NH_3$-N concentrations in 2002. The NH3-N concentrations dropped by 0.332∼0.583 mg/L on average during January∼March as outflow increases from 5 to 50cms, and was most significant on February. The results of this research show that the multiple regression approach has potential for efficient cause and effect analysis between dam outflow and downstream water quality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.537-545
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• 2018

Determination of the time parameters such as the travel time in the design flood is very important. The travel time is mainly used for flood and river management, and the travel time of non flood season is used for maintenance flow and management of the river. Estimation of travel time for natural rivers is mainly based on the geomorphological factors of the basin. In addition to the topographical factors, the travel time is calculated by considering the factors of the runoff curve, velocity and rainfall intensity. However, there is no study on the estimation of travel time considering both the rainfall condition and the soil moisture accounting by the frequency period. Therefore, the travel time calculation is divided into the case of setting the Hwanggang Dam and the Imjin bridge water level station of Imjin river as the natural river considering rainfall condition by the frequency period and the soil moisture accounting, and the case of traveling the Imjin bridge water level station according to the condition of outflow of the Hwanggang Dam. For the sections set as natural rivers, the results were verified by comparing with the newly developed travel time calculation method. Based on the results, the travel times of the Hwanggang Dam outflow conditions were calculated. The time to travel in this study can be secured flood control of the Imjin river basin and time to prepare for danger when outflowing the the Hwanggang Dam.

• Journal of Wetlands Research
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• v.2 no.1
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• pp.87-94
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• 2000

The purpose of this study was to analysis the pollutant loading of Chin yang Reservoir according to the variation of outflow. Regression equation of the pollutant loading and outflow was represented as $L=a\;Q^b$ in which L = pollutant loading(kg/day), a and b = regression coefficient, and Q = outflow($m^3/day$). Regression coefficients ($R^2$) of Sanchung, Sinan and Changchon site was in range of 0.8376 to 0.9818. Therefore the pollutant loading was good correlated with outflow. Changchon site had minimum b value because outflow of pollutant was little compared with rainfall. The SS was the highest b value 1.621~1.7834 among water quality parameters because the pollutant loading of SS was much affected by outflow. Also, the pollutant loadings per area could be calculated and compared in case of the dry season, normal season and flood season. The pollutant loading in the normal and flood season except the dry season were higher in order of Sanchung, Sinan and Changchon site. Pollutant loading per area were higher in order of Sinan, Sanchung and Changchon site. When it compared with pollutant loading per area calculated using pollutant unit loading, T-N was much different each other.

• Journal of Environmental Science International
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• v.17 no.11
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• pp.1281-1293
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• 2008

This study aims at the estimation of dam failure time and dam failure scenario analysis of and applied to Yeoncheon Dam which was collapsed August 1st 1999, using HEC-HMS, DAMBRK-FLDWAV simulation model. As the result of the rainfall-runoff simulation, the lancet flood amount of the Yeoncheon Dam site was $10,324\;m^3/sec$ and the total outflow was $1,263.90\;million\;m^3$. For the dam failure time estimation, 13 scenarios were assumed including dam failure duration time and starting time, which reviewed to the runoff results. The simulation time was established with 30 minutes intervals between one o'clock to 4 o'clock in the morning on August 1, 1999 for the setup standard for each case of the dam failure time estimation, considering the arrival time of the flood, when the actually measured water level was sharply raising at Jeongok station area of the Yeoncheon Dam downstream, As results, dam failure arrival time could be estimated at 02:45 a.m., August 1st 1999 and duration time could be also 30 minutes. Those results and procedure could suggest how and when dam failure occurs and analyzes.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.495-498
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• 2003

In order to analyze water supply capacity in estuary barrage dam, a system was developed in which base model was consisted of daily water balance model and daily inflow model. Agricultural water demand to paddy fields and domestic and industrial water demand were considered in this daily water balance model. Also outflow volume through sluice gate and inside water level at time to start outflow was conditioned initially to simulate reservoir storage. The DAWAST model was selected to simulate daily reservoir inflow in which return flows from agricultural, domestic and industrial water were included to simulate runoff. Using this developed system, water supply capacity in the Keum river estuary reservoir was analyzed.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.751-752
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• 2005

• Water Engineering Research
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• v.3 no.2
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.293-301
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• 2020

This study determined appropriate threshold level (cumulative period and percentage) of precipitation for drought management in dam basin. The 5 dam basins were selected, the daily dam storage level and daily precipitation data were collected. MAP (Mean Areal Precipitation was calculated by using Thiessen polygon method, and MAP were converted to accumulated values for 6 cumulative periods (30-, 60-, 90-, 180-, 270-, and 360-day). The correlation coefficient and ratio of variation coefficient between storage level and MAP for 6 cumulative periods were used to determine the appropriate cumulative period. Correlation of cumulative precipitation below 90-day was low, and that of 270-day was high. Correlation was high when the past precipitation during the flood period was included within the cumulative period. The ratio of variation coefficient was higher for the shorter cumulative period and lower for the longer in all dam, and that of 270-day precipitation was closed to 1.0 in every month. ROC (Receiver Operating Characteristics) analysis with TLWSA (Threshold Line of Water Supply Adjustment) was used to determine the percentage of precipitation shortages. It is showed that the percentage of 270-day cumulative precipitation on Boryung dam and other 4-dam were less than 90% and 80% as threshold level respectively, when the storage was below the attention level. The relationship between storage and percentage of dam outflow and precipitation were analyzed to evaluate the impact of artificial dam operations on drought analysis, and the magnitude of dam outflow caused uncertainty in the analysis between precipitation and storage data. It is concluded that threshold level should be considered for dam drought analysis using based on precipitation.

• Journal of Korean Society on Water Environment
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• v.30 no.1
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• pp.44-59
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• 2014

Despite the implementation of TMDL, the water quality in lower watershed of Nam river dam has worsened continuously since 2005. Multifarious pollution sources such as cities and industrial districts are scattered around it. Nam river downstream bed slope is very gentle towards the downstream water flow of slows it down even more, depending on the water quality deterioration is accelerated eutrophication occurs. In this study, the mainstream in lower watershed of Nam river dam region to target aquatic organic matter by phytoplankton growth contribution was evaluated by statistical analysis. and statistical evaluation of water quality and the accuracy of forecasting, model calibration and verification procedures by completing QUALKO2 it's eutrophic phenomena that occur frequently in the dam outflow through scenarios predict an increase in water quality management plans to present the best should.