• Journal of Environmental Science International
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• v.19 no.6
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• pp.771-778
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• 2010

The prediction of discharge is very important in water resources management and plan. In this study, we have analyzed discharge data of site at up and down stream in watershed. In order to forecast discharge the regression equations were developed by measuring flow data. Also, to forecast the change of water quality followed by change of inflow the correlation relationship between inflow of the Youngchun site and the Chunhju dam was shown as very high. The forecast of inflow at the Chungju dam would be possible through flow analysis of the Youngchun site. And, it is possible to forecast water quality by flow analysis because the correlation relationship of SS and turbidity followed by change of flow for each station of investigation was very high.

• Journal of Korean Society on Water Environment
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• v.33 no.2
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• pp.219-229
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• 2017

A watershed-river linked modeling system was developed to forecast the water quality, particularly weekly changes in chlorophyll-a concentration, of the Yeongsan River, Korea. Hydrological Simulation Program-Fortran (HSPF) and Environmental Fluid Dynamics Code (EFDC) were adopted as the basic model framework. In this study, the EFDC model was modified to effectively simulate the operational condition and flow of multi-functional weirs constructed in the main channel of rivers. The model was tested against hydrologic, water quality and algal data collected at the right upstream sites of two weirs in 2014. The mean absolute errors (MAEs) of the model calibration on the annual variations of river stage, TN, TP, and algal concentration are 0.03 ~ 0.10 m, 0.65 ~ 0.67 mg/L, 0.03 ~ 0.04 mg/L, and $9.7{\sim}10.8mg/m^3$, respectively. On the other hand, the MAE values of forecasting results for chlorophyll-a level at the same sites in 2015 range from 18.7 to $22.4mg/m^3$, which are higher than those of model calibration. The increased errors in forecasting are mainly attributed to the higher uncertainties of weather forecasting data compared to the observed data used in model calibration.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• Journal of Korean Society of Water and Wastewater
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• v.9 no.4
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• pp.73-86
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• 1995

The propriety of the numerical model application was examined on Paldang resevoir and its inflow tributaries located in the center of the Korean peninsula and the long term water quality forecast of the oxygen profile was carried out in this syduy. The input data of the model was the capacity of the reservoir, catchment area, percolation, diffusion rate, vertical mixing rate, dissolution rate from the bottom of the reservoir, outflow of the resevoir, water quality measurement and meteorology data of the drainage basin, and the output result was the annual estimation value of the dissolved oxygen concentration and the biochemical oxygen demand. The modeling method is based on the measured or calculated boundary condition dividing the water area into several blocks from the macorscopic aspect and considering the mass balance in these blocks. As the result of the water quality forecast, it was expected that the water quality in Northern Han River and Paldang reservoir would maintain the recent level, but that the water quality in the Southern Han River and its inflow tributary would worsen below the grade 4 of the life environmental standard from around 2000 owing to the decrease of DO concentration and the increase of BOD concentration.

• Journal of Korean Society on Water Environment
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• v.32 no.6
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• pp.570-581
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• 2016

A watershed model was constructed using the Hydrological Simulation Program Fortran to predict the water quality, especially chlorophyll-a concentraion, at major tributaries of the Nakdong River basin, Korea. The BOD export loads for each land use in HSPF model were estimated at $1.47{\sim}8.64kg/km^2/day$; these values were similar to the domestic monitoring export loads. The T-N and T-P export loads were estimated at $0.618{\sim}3.942kg/km^2/day$ and $0.047{\sim}0.246kg/km^2/day$, slightly less than the domestic monitoring data but within the range of foreign literature values. The model was calibrated at major tributaries for a three-year period (2008 to 2010). The deviation values ranged from -31.5~1.6% of chlorophyll-a, -24.0~2.2% of T-N, and -5.7~34.8% of T-P. The root mean square error (RMSE) ranged from 4.3~44.4 ug/L for chlorophyll-a, -0.6~1.5 mg/L for T-N, and 0.04~0.18 mg/L for T-P, which indicates good calibration results. The operational water quality forecasting results for chlorophyll-a presented in this study were in good agreement with measured data and had an accuracy similar with model calibration results.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• Journal of Korean Society on Water Environment
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• v.20 no.4
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• pp.321-326
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• 2004

The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.99-119
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• 2018

Unit load factor, which is used for the quantification of non-point pollution in watersheds, has the limitation that it does not reflect spatial characteristics of soil, topography and temporal change due to the interannual or seasonal variability of precipitation. Therefore, we developed the method to estimate a watershed-scale non-point pollutant load using seasonal forecast data that forecast changes of precipitation up to 6 months from present time for watershed-scale water quality management. To establish a preemptive countermeasure against non-point pollution sources, it is possible to consider the unstructured management plan which is possible over several months timescale. Notably, it is possible to apply various management methods such as control of sowing and irrigation timing, control of irrigation through water management, and control of fertilizer through fertilization management. In this study, APEX-Paddy model, which can consider the farming method in field scale, was applied to evaluate the applicability of seasonal forecast data. It was confirmed that the rainfall amount during the growing season is an essential factor in the non-point pollution pollutant load. The APEX-Paddy model for quantifying non-point pollution according to various farming methods in paddy fields simulated similarly the annual variation tendency of TN and TP pollutant loads in rice paddies but showed a tendency to underestimate load quantitatively.

• Journal of Korean Society on Water Environment
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• v.30 no.6
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• pp.673-682
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• 2014

A watershed model was constructed using Hydrological Simulation Program Fortran to predict the water temperature at major tributaries of Nakdong River basin, Korea. Water temperature is one of the most fundamental indices used to determine the nature of an aquatic environment. Most processes of an aquatic environment such as saturation level of dissolved oxygen, the decay rate of organic matter, the growth rate of phytoplankton and zooplankton are affected by temperature. The heat flux to major reservoirs and tributaries was analyzed to simulate water temperature accurately using HSPF model. The annual mean heat flux of solar radiation was estimated to $150{\sim}165W/m^2$, longwave radiation to $-48{\sim}-113W/m^2$, evaporative heat loss to $-39{\sim}-115W/m^2$, sensible heat flux to $-13{\sim}-22W/m^2$, precipitation heat flux to $2{\sim}4W/m^2$, bed heat flux to $-24{\sim}22W/m^2$ respectively. The model was calibrated at major reservoir and tributaries for a three-year period (2008 to 2010). The deviation values (Dv) of water temperature ranged from -6.0 to 3.7%, Nash-Sutcliffe efficiency(NSE) of 0.88 to 0.95, root mean square error(RMSE) of $1.7{\sim}2.8^{\circ}C$. The operational water temperature forecasting results presented in this study were in good agreement with measured data and had a similar accuracy with model calibration results.

• Journal of Environmental Science International
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• v.9 no.6
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• pp.455-462
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• 2000

This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.