• 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 Korean Society on Water Environment
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• v.25 no.1
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• pp.125-135
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• 2009

Reliable long-term flows by SWAT-K model were applied to the relationship between stream flow and pollutant load derived from 8-day measured data of Ministry of Environment (MOE) in order to obtain continuous loadograph and evaluate accuracy in water quality modeling for the Chungju dam watershed. The measured flow were compared with flow duration curve from the model, and it showed that measured values corresponded to the almost full range of stream flow conditions except at Odae A. And there was significant relationship ($R^2=0.60{\sim}0.97$) between measured flow and water quality load at all unit-watersheds. Applying this relationship to simulated flows, continuous loadograph was obtained and compared with modeled pollutant loads. Although there were some differences during some dry and flood seasons, those were not significant and overall trend showed a good agreement. From the results, we would be able to derive a continuous loadograph based on measured data at total maximum daily loads (TMDLs) unit-watersheds on a national scale, in which stream flow and water quality have been measured at 8-day intervals since 2004, and this could be helpful to utilize distributed water quality models with difficulty in calibrating and validating parameters from lack of measured data at present.

• Spatial Information Research
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• v.4 no.2
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• pp.147-157
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• 1996

A rural water quality management information system(RWQMIS) by integrating Geo¬graphic Information System(GIS) with the existing models (pollutants transport and river water quality) is described. A simple pollutant load model to calculate delivered pollutants to stream, Tank model to generate daily runoff and QUAL2E model to predict river water quality, were incorporated into GIS. The system was applied to $80km^2$ watershed in Icheon Gun and Yongin Gun, Kyonggi Do. The spatial distributions of produced pollutant load, discharged pollutant load, delivered ratio to the stream, and the river water quality status for given sites were successfully generated.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.313-322
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• 1998

The distributions of chemical oxygen demand (COD) and suspended solid (SS) in Ulsan Bay were simulated and reproduced by a numerical ecosystem model for the practical application to the management of marine water quality and the prediction of water quality change due to coastal developments or the constructions of breakwater and marine facilities. Comparing the computed with the observed data of COD and SS in Ulsan bay the results of simulation were found to be good enough to satisfy the practical applications.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.509-514
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• 2005

Geumgang canal is planned to connect Geumgang lake with Saemangeum reservoir to accelerate desalinization and dillute polluted water in Saemangeum reservoir. The purpose of this study is to evaluate the variations of water quality by divesion of Geumgang lake flow to the Saemangeum reservoir. WASP5 model was used to estimate water quality concentration of Geumgang lake. Model calibration and verification was done for water quality data for 2001 and 2002. As a result of simulating water quality concentration for 4 scenarios, which was considered whether Geumgang canal will be built, there was little influence on water quality in Geumgang lake though Geumgang lake flow diverted to Saemangeum reservoir.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1395-1408
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• 2014

In this research, a River Recreation Index Model (RRIM) was developed to provide sufficient information on the water quality of rivers to the public in order to secure safety of publics. River Recreation Index (RRI) is an integrated water quality information for recreation activities in rivers and expressed as the point from 0 to 100. The proposed RRIM consisted of two sub models: Fecal Coliform Model (FCM) and Water Quality Index Model (WQIM). FCM predicted Fecal Coliform Grade (FCG) using a logistic regression and WQIM synthesized water quality parameters of, DO, pH, turbidity and chlorophyll a into Water Quality Index (WQI). FCG and WQI were integrated into RRI by the integrating algorithm. The proposed model was applied to upstream of Gangjeong Weir in Nakdong River, and compared with Real Time Water Quality Index (RTWQI) which is the existing water quality information system for recreation use. The results show that calculated RRI reflected change of integrated water quality parameters well. Especially chlorophyll a showed Pearson correlation coefficient -0.85 with RRI. Also, RRIM produced more conservative index than RTWQI because RRI was calculated considering uncertainty of water quality criteria. Further, RRI showed especially low values when fecal coliform was predicted as low grade.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.3
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• pp.118-124
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• 1998

To use chlorine residual as an surrogate parameter of the water quality change during the transportation in the water distribution system(WDS), the correct prediction model of chlorine residual must be established in advance. This paper shows the procedure and the result of applying the water quality model to the field WDS. To begin with, hydraulic model was calibrated and verified using fluoride as an tracer. And chlorine residual was predicted through simulation of water quality model. This predicted value was compared with the observed value. With adjusting the bulk decay coefficient(kb) and the wall decay coefficient(kw) according to the pipewall environment, the predicted chlorine residual can represent the observed value relatively well.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• 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.