• 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.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.30 no.1
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• pp.16-24
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• 2014

A combined watershed and receiving waterbody model was developed for operational water flow forecasting of the Nakdong river. The Hydrological Simulation Program Fortran (HSPF) was used for simulating the flow rates at major tributaries. To simulate the flow dynamics in the main stream, a three-dimensional hydrodynamic model, EFDC was used with the inputs derived from the HSPF simulation. The combined models were calibrated and verified using the data measured under different hydrometeological and hydraulic conditions. The model results were generally in good agreement with the field measurements in both calibration and verification. The 7-days forecasting performance of water flows in the Nakdong river was satisfying compared with model calibration results. The forecasting results suggested that the water flow forecasting errors were primarily attributed to the uncertainties of the models, numerical weather prediction, and water release at the hydraulic structures such as upstream dams and weirs. From the results, it is concluded that the combined watershed-waterbody model could successfully simulate the water flows in the Nakdong river. Also, it is suggested that integrating real-time data and information of dam/weir operation plans into model simulation would be essential to improve forecasting reliability.

• 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 on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of 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 qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.

• Journal of Environmental Science International
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• v.7 no.4
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• pp.433-440
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• 1998

This study was carried out to develop the stream water quality model for the intaking station of Kongju waterworks in the Keum River system. The monthly water quality(total nitrogen and total phosphorus) with periodicity and trend were forecasted by multiplicative ARIU models and then the applicability of the models was tested based on 7 years of the historical monthly water quality data at Kongju intaking strate. The parameter estimation was made with the monthly observed data. The last one year data was used to compare the forecasted water Quality by ARU model with the observed one. The models are ARIMA(2,0,0)$\times$(0,1,1)l2 for total nitrogen, ARIMA(0,1,1)x(0,1,1)l2 for total phosphorus. The forecasting results showed a good agreement with the observed data. It is implying the applicability of multiplicative ARIMA model for forecasting monthly water quality at the Kongju site.

• Journal of Environmental Science International
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• v.11 no.3
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• pp.201-207
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• 2002

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.

• Journal of Environmental Impact Assessment
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• v.14 no.2
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• pp.55-62
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• 2005

This study applied the Neural Network and Fuzzy theory to show water-purity control and preventive measure in water quality forecasting of the future river. This study picked out NAJU and HAMPYUNG as the subject of investigation and used monthly the water quality and the outflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999 to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for study and that of 2000 are used for verification. To develop model of water quality forecasting, firstly, this research formed Neural Network model and divided Neural Network model into two case - the case of considering lag and not considering. And this study selected optimal Neural Network model through changing the number of hidden layer based on input layer(n) from n to 3n. Through forecasting result, the case without considering lag showed more precise simulated result. Accordingly, this study intended to compare, analyse that Fuzzy model using the method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.701-711
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• 2001

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 Gongju station in Geum River. This is done by forecasting monthly water qualities such as DO, BOD, and TN, and comparing with those obtained by ARIMA model. The neural network models of this study use BP(Back Propagation) algorithm for training. In order to improve the performance of the training, the models are tested in three different styles ; MANN model which uses the Moment-Adaptive learning rate method, LMNN model which uses the Levenberg-Marquardt method, and MNN model which separates the hidden layers for judgement factors from the hidden layers for water quality data. the results show that the forecasted water qualities are reasonably close to the observed data. And the MNN model shows the best results among the three models tested