• Journal of Korean Society on Water Environment
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• v.25 no.5
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• pp.651-660
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• 2009

Detailed flow analysis in river is essential to increase the accuracy of water quality simulation since flow variation depends on many factors such as cross sections, channel slopes, and bed materials. In the QUAL2E stream water quality simulation model, the hydraulic coefficients are assigned to the reach that is collection of computational element using the hydraulic coefficient. This study developed a module that can incorporate the results of non-uniform flow analysis and assign such information to each individual element. Model application focused on the upstream of the Hoengseong reservoir including the reservoir where significant flow change is expected. Comparing with original QUAL2E model the developed module improved the result of water quality simulation without considering the relation of flow velocity and flow depth in terms of flow rates.

• 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 on Water Environment
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• v.20 no.6
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• pp.685-691
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• 2004

Water quality forecasting with long term flow is important for management and operation of river environment. However, it is difficult to set up and operate a physical model for water quality forecasting due to large uncertainty in the data required for model setting. Therefore, relatively simpler stochastic approaches are adopted for this problem. In this study we try several multivariate time series models such as ARMAX models for the possible substitute for water quality forecasting. Those models are applied to the BOD and COD levels at Noryangin station, Han river, and also evaluated the effect of release from Paldang dam on them. Monthly BOD and COD data from 1985 to 1991 (7 years) are used for model building and another two year data for model testing. As a result of the study, the effect of improvement on water quality is much more effective combining with the water quality improvement of dam release than considering only increment of dam release in the downstream Han river.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2000.10a
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• pp.246-252
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• 2000

The water quality in Yeongil Bay is getting worse due to the sewage and the waste water from the surrounding industrial complex The study aims to simulate the current system that is necessary to built ecosystem model for the optium water quality control and clarify the correlation of current system characteristics with water quality in Yongil Bay. To clarify the characteristics of coastal water movement system and verify the applicability of the 3-D model, the current system was simulated using 3-D baroclinic model considered tidal current and density effects. As the results of numerical experiments, it is proved the 3-D model is the most appliable on the Yongil Bay where current flows slowly and the flow direction is varied by depths. From the results of simulation considered tidal current only, It am be clearly said the water in Yongil Bay flows in through the surface layer and flows out through the bottom layer. And the fresh water from the Hyongsan river and the heated discharge from POSCO have little effect on the current structure in Yonggil Bay, but have and important effect upon the density structure by diffusion of heat and salt. And the water quality distribution is closely related with the current structure characteristics as well as the tidal residual current system.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Water for future
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• v.21 no.4
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• pp.407-414
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• 1988

The purpose of this paper is to study the following ; (1) how the stability and sensitivity of a given stream water quality model can be analyzed theoretically by means of the stability theory and the sensitivity theory, and (2) point out that the results of this study prove that numerical analysis for the given stream water quality model is reliable, and the model is sensitive for the variations of parameters. A stability theory which is described by the infinite Fourier series is used to analyze the numerical scheme of the model. The numerical shheme is used a backward implicit scheme. a sensitivity theory which is described by the first order linear vector equation is used to analyze theoretically the effect of variations of water quality parameters such as BOD loads, flow rate, temperature. The results of sensitivity theory are of general applicability and are presented in a analytical form. The results of this study seems to be satisfactory for the reliability of stream water quality model with respect to the numerical scheme and the variations of the water quality parameters.

• 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 Environmental Health Sciences
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• v.31 no.6
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• pp.489-497
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• 2005

A numerical method for the mathematical water modeling in 2-dimensional flow has been developed. The model based on a split operator technique, in which, the advection term is calculated using the upwind scheme. The diffusion term is one- dimensionalized and calculated using Crank-Nicholson's implicit finite difference scheme to reduce the numerical errors from large time steps and variable spacings. It also provides a relatively simple and economic method for more accurate simulation of pollutant dispersion. Water depths and flow velocities in the Boreyong reservoir during the normal water periods were predicted by numerical experiments with a 2-dimensional flow model so as to provide current field data for the study of advection and diffusion of pollutants. Developed 2-dimensional water quality model is applied to Boreyong reservoir to simulate a spatial and periodical changes of water quality.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Journal of Korean Society on Water Environment
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• v.22 no.6
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• pp.1144-1150
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• 2006

Recently, as water pollution accidents in rivers have increased, there is an increased interest in water quality forecast with accurate simulation. QUAL2E model, widely used for water quality analysis, uses the same hydraulic characteristics, such as depth and velocity, in a reach. The flow of the river is changed by various hydraulic constructions or by topography in a real river channel. In this study, a hydraulic connection module is developed to consider flow variations of river channels in QUAL2E model. The module uses the simulations results of non-uniform flow of a 1-D hydraulic model such as DWOPER or HEC-RAS. The improved QUAL2E model with this module was applied to a downstream section of Paldang Dam on the Han River. The results show the variation of water quality very well in a reach where flowing vary abruptly, like the Jamsil submerged weir.