• 한국물환경학회지
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• 제24권3호
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• pp.345-353
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• 2008

In order to design storage-based non-point source management facilities, the aspect of statistical features of the entire precipitation time series should be considered since non-point source pollutions are delivered by continuous rainfall runoffs. The 3-parameter mixed exponential probability density function instead of traditional single-parameter exponential probability density function is applied to represent the probabilistic features of long-term precipitation time series and model urban stormwater runoff. Finally, probability density functions of water quality control basin overflow are derived under two extreme intial conditions. The 31-year continuous precipitation time series recorded in Busan are analyzed to show that the 3-parameter mixed exponential probability density function gives better resolution.

• 한국환경보건학회지
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• 제41권2호
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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.

• 한국물환경학회지
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• 제36권6호
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.344-352
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• 1993

The purpose of this study is to investigate the possibility of the non-destructive quality evaluation for food by the acoustic non-linear parameter B/A which is a measure of the non-linearity of the state equation of the medium in terms of pressure and density. The B/A of water, corn oil O/W(oil in water) emulsion and milk were measured by using a sound velocity measuring system. The B/A value of water was measured for ascertaining reliability of our experimental system. Corn oil W/W emulsion was prepared as a model of milk . It was proved that the B/A value of O/W emulsion was related to the oil concentration by a law of mixture. We applied this result to milk and obtained satisfactory results for predicting the milk fat concentration.

• 대한토목학회논문집
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• 제26권3B호
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• pp.291-300
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• 2006

본 연구에서는 통계를 이용한 경안천유역의 최적 수질 측정망 구성이 제안된다. 분석을 위해서 필요한 수질 데이터는 QUAL2E 모형을 이용하여 모의하였으며, 경안천 유역의 2000년도 3월부터 11월까지의 월 평균자료를 이용하였다. QUAL2E 모형을 구축하는데 필요한 수리 매개변수는HEC-RAS모형을 이용하여 추정하였다. 수질매개변수의 경우 월평균 실측자료를 바탕으로 1차 신뢰성 분석(FORA)를 이용하여 민감도 분석을 실시하여 수질항목별로 민감하지 않은 매개변수를 제외한 후 보정이 이루어진다. QUAL2E 모형의 모의 결과를 바탕으로 크리깅 기법과 Branch and Boundary Method를 이용하여 평수량 일때와 갈수량 일때로 구분하여 관측지점의 개수와 위치가 결정된다. 또한 선정된 지점을 기준으로 proportional sampling method(비례표본추출법)를 이용하여 각각의 지점별 측정 빈도가 제시된다.

• 한국환경과학회지
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• 제17권6호
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• pp.611-621
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• 2008

This study intends to provide the necessary basic data needed for predicting the water quality and examining changes in water quality on the basis of the hydrological changes: an outflow or the character of a flow by investigating the interaction of the parameters through the estimation of optimal parameters need for predicting the water quality of the dam basin and the sensitivity among those estimated parameters. Im-Ha Dam in the upstream area of the Nakdong River was selected for analysis, and the water quality survey data necessary for parameter estimation was based on the monthly water quality data (water temperature, BOD, T-N and T-P) between December 1, $2005{\sim}$November 31, 2006. K1C(the saturated growth rate of plant plankton), K1RC (endogenous respiratory quotient of plankton), KDC(deoxidized ratio), K71C(minealized ratio of dissolved organic phosphorus), K83C(mineralized ratio of dissolved organic nitrogen) have been considered as the factors of the water quality performed in this water quality simulation, that is, the most effective parameters on BOD, T-N and T-P. In the result of the analysis of the sensitivity, KDC(deoxidized ratio) was the most sensitively reacted parameter on BOD and it was K71C(mineralized ratio of dissolved organic phosphorus) and K83C(mineralized ratio of dissolved organic nitrogen) on T-N and T-P. It is considered that it will be possible to apply the most optimal parameter to an analysis of the water quality simulation at Im-Ha Ho basin in the goal year by examining the interaction of the parameters through the parameters sampling which are able to applicable to prediction of the water quality and the analysis of the its sensitivity, in the future, also the analysis on the basis of the hydrological conditions: an outflow or the character of a flow will be needed.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.287-287
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• 2023

Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

• Environmental Engineering Research
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• 제16권3호
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• pp.121-129
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• 2011

The completely mixed flow and plug flow (CAP) water quality model was developed for streams with discontinuous flows, a condition that often occurs in low base flow streams with in-stream hydraulic structures, especially during dry seasons. To consider the distinct physical properties of each reach effectively, the CAP model stream network can include both plug flow (PF) segments and completely mixed flow (CMF) segments. Many existing water quality models are capable of simulating various constituents and their interactions in surface water bodies. More complicated models do not necessarily produce more accurate results because of problems in data availability and uncertainties. Due to the complicated and even random nature of environmental forcing functions, it is not possible to construct an ideal model for every situation. Therefore, at present, many governmental level water quality standards and decisions are still based on lumped constituents, such as the carbonaceous biochemical oxygen demand (CBOD), the total nitrogen (TN) or the total phosphorus (TP). In these cases, a model dedicated to predicting the target concentration based on available data may provide as equally accurate results as a general purpose model. The CAP model assumes that its water quality constituents are independent of each other and thus can be applied for any constituent in waters that follow first order reaction kinetics. The CAP model was applied to the Geum River in Korea and tested for CBOD, TN, and TP concentrations. A trial and error method was used for parameter calibration using the field data. The results agreed well with QUAL2EU model predictions.

• 한국물환경학회지
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• 제21권6호
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• pp.575-583
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• 2005

In this study the flow curve estimation is discussed using TANK model which is one of hydrologic models. The main interest is the accuracy of TANK model parameter estimation with respect to the sampling frequency of input data. For doing this, input data with various sampling frequencies is used to estimate model parameters. As a result, in order to generate relatively accurate flow curve, it is recommendable to measure stream flow at least every 8 days.

• 한국물환경학회지
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• 제39권1호
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.