• Proceedings of the Korea Water Resources Association Conference
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• 1993.07a
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• pp.159-165
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• 1993

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.632-637
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• 2001

• Water for future
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• v.17 no.1
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• pp.31-36
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• 1984

This study has performed to investigate and evaluate the simulation model of steam Water Quality and the simulated results have 매내 been compared with the observed data in the Han River. The predicted BOD, Total-N, Coliform concentrations in the downstream of the Chungrang-Cheon are 8.6m/1, 4.5mg/1 and $3.7X10^5$ respectively. It is interesting to note that the results simulated based on the WQRRS model are extremely in good agreement and also are very much comparable with those observed data reported previously references.

• Environmental Engineering Research
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• v.10 no.2
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• pp.62-70
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• 2005

Pollution loading of Combined Sewer Overflows (CSOs) is frequently over the capacity of a wastewater treatment plant (WWTP) receiving the water. The objectives of this study are to investigate water quality of CSOs in Anmyun-ueup, Tean province and to apply Storm Water Management Model to predict flow rate and water quality of the CSOs. The capacity of a local WWTP was also estimated according to rainfall duration and intensity. Eleven water quality parameters were analyzed to characterize overflows. SWMM model was applied to predict the flow rate and pollutant load of CSOs during rain event. Overall, profile of the flow and pollutant load predicted by the model well followed the observed data. Based on model prediction and observed data, CSOs frequently occurs in the study area, even with light precipitation or short rainfall duration. Model analysis also indicated that the local WWTP’s capacity was short to cover the CSOs.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1387-1391
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• 2007

In this study, water quality prediction that is necessary to water quality forecasting system is performed using 2-D river analysis models RMA-2 and RAM4. RAM4 is suitable to water quality forecasting system cause it is possible to put in the pollutants as a mass type boundary condition. Instant injections of pollutants at Yongdamdaegyo Bridge in Namhangang River are simulated and the behavior of pollutant cloud is observed. The effects of water quality accident to Paldang 2 water intake plants in Paldangho Lake is analyzed with time variation. And extra flow simulation is performed for mitigation of pollution. Several cases of water quality forecasting system at home and abroad are investigated and the direction of water quality forecasting system is presented.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.200-207
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• 2010

The sewage treatment plants to be built to improve the water quality of the Mankyung River will total 11, of which combined capacity will reach $39,850m^3/day$, and saying in detail, 5 at Gunsan city, 2 at Iksan city, 1 at Kimje city and 3 at Wanju gun, The scenario for water quality improvement was developed, considering the conditions of plant operation ratio and the accomplishment of the water quality target (BOD 4.4 mg/L, T-P 0.356 mg/L) at the end of the watershed of Mankyung B was predicted, making use of QUAL2E model. As a result of prediction using QUAL2E model based on scenarios with 70% and 100% of operation ratio, respectively, at 11 plants in 2010, the water quality at the watershed of Mankyung B was estimated at 4.322 mg/L which was lower than the target of BOD 4.4 mg/L, indicating the target water quality was achieved, when it comes to 70% of operation ratio, But in case of T-P, it was estimated at 0.565 mg/L, which was higher than the target. When it comes to 100% of operation ratio, T-P also was 0.563 mg/L which exceeded the target, 0.356 mg/L. As indicated above, the effect of water quality improvement appeared very insignificant, which was attributable to the limit of small scale sewage treatment plant in total reduction capacity. Hence, the measures for additional reduction in a bid to achieve the target water quality of T-P at the designated location need to be taken, and the measures to build the Sewage treatment facilities at the place where the pollution is significantly caused by T-P appeared to be required as well.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.49-56
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• 2001

The water quality simulation was carried out to predict water quality in Tan stream of the Han river using water quality model, QUAL2E. In the end, the future variations in water quality of Tan stream were simulated and the prediction of the impacts of Tan stream on water quality in the Han river was carried out by applying the Tan stream simulation results into the model. The results are as follows. The predicted results of future water quality of Tan stream suggested that the concentrations of BOD, T-N and T-P at Chungdam bridge would increase to 0.68~0.77 mg/$\ell$, 1.33~1.62 mg/$\ell$ and 0.05~0.06 mg/$\ell$, respectively in 2006 and 2011 and that with the implementation of advanced treatment in Sungnam and Tanchun sewage treatment plants, the concentration of T-N would be reduced more as the amount of treated sewage increase, while the concentration of T-P would stay 0.49 mg/$\ell$. The results obtained from simulation of the impacts of future Tan stream water quality improvement on the main stream of the Han river showed that with implementation of advanced treatment in both Sungnam and Tanchun sewage treatment plants, the concentration of T-N, T-P and chlorophyll-a at Hangang bridge and Heangju bridge would be reduced by 11.6%, 7.7% and 20.9%, respectively in 2..6 and by 13.6%, 9.4% and 22.2%, respectively in 2011, which indicates that the effect on the reduction of T-N and T-P would be relatively significant while the effect on the decrease of algae would be slight.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.