• 한국물환경학회지
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• 제28권2호
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• pp.178-184
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• 2012

BOD decay rate is a key parameter of BOD-DO models in streams and lakes. In the calibration of water quality modeling appropriate range of coefficient is required for guidance of parameter selection. In this study BOD decay rate was measured at 48 stream sites and 10 reservoir sites in 8 different river systems. The decay rate ranged from 0.09 to 0.25 $day^{-1}$ with a mean of 0.16 $day^{-1}$. Among river systems the decay rates showed significantly different ranges, with the Han River system showing higher values than other river systems. In comparing different types of water bodies, the decay rate was slightly higher in tributaries than in reservoirs and mainstreams. Our results can provide guidance to the selection of proper coefficient for various water bodies in the calibration of water quality models.

• Membrane and Water Treatment
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• 제10권1호
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• pp.1-11
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• 2019

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the $21^{st}$ century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.

• 한국물환경학회지
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• 제25권1호
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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.

• 한국물환경학회지
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• 제29권1호
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• pp.19-28
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• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

• Journal of Biosystems Engineering
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• 제39권4호
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• 한국안전학회지
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• 제27권5호
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• pp.135-140
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• 2012

In this study, the behavior of pollutant was calculated at Paldang reservior according to several scenarios of the accidental water pollution by means of the numerical models for forecasting water quality. Also managemental plans for situation of the accidental water pollution happening at Paldang watershed were simulated. According to the simulating results, a plan of increase of discharge at Cheongpyeong dam reduced the peak concentration of pollutants, whereas extended the time for stopping water intake. Another plan, drop of water elevation at Paldang dam, decreased seriously the time for stopping water intake although there were a little effect to decrease the peak concentration. Thus it was concluded that appropriate combinations of the plans for the increase discharge and the dropping water elevation should be used to deal with the accidental water pollution at Paldang watershed.

• 한국농공학회논문집
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• 제61권2호
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• pp.25-39
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• 2019

The water pollution Accident in the South Han River is increasing due to increase of pollutants inflow from small streams from rural areas and reduced flow rate. This study predicted the change of water quality in the main stream of the South Han River due to climate change through the linkage of watershed and water quality models. Also, This study analyzed the effect of water quality improvement on Seomgang and the South Han River by securing the flow during the dry season. According to the scenarios for securing the river flow during drought season, the river flow in the Seomgang is increased up to 2.19 times, and the water quality during the drought season was improved up to $BOD_5$ 20.5%, T-N 40.8%, T-P 53.4%. Also, the water quality of the main stream of the South Han River improved to 5.22% of $BOD_5$, 5.42% of T-N and 7.69% of T-P as the river flow was secured from the Seomgang. The result of this study confirms that securing the baseflow in the Seomgang according to the scenarios for securing the river flow during the dry season has a positive effect on the improvement of the water quality of the rivers in the main river of the Seomgang and South Han River. The results of this study will contribute to the establishment of reasonable management to improve the water quality of the main stream of the Seomgang and South Han River.

• 상하수도학회지
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• 제28권3호
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• pp.287-297
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• 2014

It is important to predict chlorine decay with different water purification processes and distribution pipeline materials, especially because chlorine decay is in direct relationship with the stability of water quality. The degree of chlorine decay may affect the water quality at the end of the pipeline: it may produce disinfection by-products or cause unpleasant odor and taste. Sand filtrate and dual media filtrate were used as influents in this study, and cast iron (CI), polyvinyl chloride (PVC), and stainless steel (SS) were used as pipeline materials. The results were analyzed via chlorine decay models by comparing the experimental and model parameters. The models were then used to estimate rechlorination time and chlorine decay time. The results indicated that water quality (e.g. organic matter and alkalinity) and pipeline materials were important factors influencing bulk decay and sand filtrate exhibited greater chlorine decay than dual media filtrate. The two-component second-order model was more applicable than the first decay model, and it enabled the estimation of chlorine decay time. These results are expected to provide the basis for modeling chlorine decay of different water purification processes and pipeline materials.

• 한국해양학회지:바다
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• 제27권2호
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• pp.71-86
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• 2022

해양환경을 정량적으로 평가하기 위해 수질평가지수(water quality index, WQI)가 사용되고 있다. 우리나라는 해양수산부고시 해양환경기준에 따라 WQI를 5개 등급으로 구분하여 수질을 평가한다. 하지만, 방대한 수질 조사 자료에 대한 WQI 계산은 복잡하고 많은 시간이 요구된다. 이 연구는 기존의 조사된 수질 자료를 활용하여 WQI 등급을 예측할 수 있는 기계학습(machine learning, ML) 기반의 모델을 제안하고자 한다. 특별관리해역인 시화호를 모델링 지역으로 선정하였다. AdaBoost와 TPOT 알고리즘을 모델 훈련을 위해 사용하였으며, 분류 모델 평가 지표(정확도, 정밀도, F1, Log loss)로 모델 성능을 평가하였다. 훈련하기 전, 각 알고리즘 모델의 최적 입력자료 조합을 탐색하기 위해 변수 중요도와 민감도 분석을 수행하였다. 그 결과 저층 용존산소(dissolved oxygen, DO)는 모델의 성능에서 가장 중요한 인자였다. 반면, 표층 용존무기질소(dissolved inorganic nitrogen, DIN)와 표층 용존무기인(dissolved inorganic phosphorus, DIP)은 상대적으로 영향이 적었다. 한편, 최적 모델의 시공간적 민감도와 WQI 등급 별 민감도를 비교한 결과 각 조사 정점 및 시기, 등급 별 모델의 예측 성능이 상이하였다. 결론적으로 TPOT 알고리즘이 모든 입력자료 조합에서 성능이 더 우수하여 충분한 자료로 훈련된 최적 모델은 새로운 수질 조사 자료의 WQI 등급을 정확하게 분류할 수 있을 거라 판단된다.

• 한국농공학회논문집
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• 제54권6호
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• pp.53-63
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• 2012

The objectives of this study were to develop a linked watershed-waterbody modeling system and to assess the impacts of indirect wastewater reuse on irrigation water quality. The Osan stream watershed within Gyeonggi-do of South Korea was selected for this study. The linked modeling system was composed of the SWAT (Soil and water assessment tool) and QUALKO2 models. The SWAT model was calibrated and validated using the stream discharge and water quality data from 2010 to 2011. Runoff and non-point source pollutants from each subbasin and stream discharge from 1980 to 2009 were simulated by the SWAT model and applied to the QUALKO2 model. The QUALKO2 model was calibrated and validated under the conditions of low water and normal discharges, respectively. Finally, The 10-day irrigation water quality from April to September was simulated. The statistical measures of coefficient of determination ($R^2$), reliability index (RI), and efficiency index (EI) were used to evaluate the system performance. The $R^2$, RI and EI values ranged from 0.5 to 1.0, 1.03 to 1.92, and -35.03 to 0.95, respectively. The 10-day irrigation water quality showed the concentrations of BOD and coliform exceeded the water quality guidelines for wastewater reuse. The linked modeling system can be a useful tool to estimate non-point source pollutant loads in watershed and to control the water quality of effluent from a wastewater treatment plant and irrigation water in the downstream waterbody.