• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.480-487
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• 2008

Spatial and temporal analyses of water qualities were performed for 11 monitoring stations located in Mangyung watershed in order to analyze the trends of monthly water quality data of Biochemical Oxygen Demand (BOD), Total Nitrogen (TN) and Total Phosphorus (TP) measured from 1995 to 2004. The long-term trends were analyzed utilizing Seasonal Mann-Kendall test, LOWESS and three-dimensional graphs were constructed with respect to distance and time. The graph can visualize spatial and temporal trend of the long-term water quality in a large river system. The results of trend analysis indicated that water quality of BOD and TN showed the downward trend. This quantitive and quantitative analysis is the useful tool to analyze and display the long-term trend of water quality in a large river system.

• Journal of Environmental Science International
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• v.4 no.3
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• pp.207-220
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• 1995

In order to study on the long-term change of water quality, water analysis was conducted at 16 sites surrounding the Kumho river system for 11 times from September 1990 to August 1993. Analytical items for the study of water Quality are as follows; water temperature, pH, COD, BOD, DO, SS, electrical conductivity, $NH_3-N$, $NO_2^-N$, NO_3^-N$, $PO_4^{3-}-P$, total-P, hardness, oil and grease, ABS, phenol, zinc, chromium, cadmium, manganese, iron, lead and color. The long-term change of water quality in the Kumho river for the period studied was found that the values of water temperature, electrical conductivity, phenol, $NO_2^-N$ and $NH_3-N$ were increasing and those of COD, BOD, SS, oil and grease, ABS, NO_3^-N$, $PO_4^{3-}-P$, copper, zinc, chromium, cadmium, manganese and lead were decreasing, while those of pH, hardness, iron and manganese were steady.

• Journal of Environmental Science International
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• v.15 no.5
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• pp.447-459
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• 2006

Long-term trends and distribution patterns of water quality were investigated in the Cheonsu Bay of Korea from 1983 to 2004. Water samples were collected at 4 stations and physicochemical parameters were analyzed including water temperature, salinity, suspended solids (SS), chemical oxygen demand (COD), dissolved oxygen (DO) and nutrients. Spatial distribution patterns were not clear between stations but the seasonal variations were distinctive except COD, SS and nitrate. Twenty two year long-term trend analysis by PCA revealed the significant changes in water quality in the study area. Water quality during 1980's and early 1990's showed high SS, low nutrients and low COD which increased during the mid and late 1990's and early 2000's. Overall water duality in the Cheonsu Bay indicated the increase in nutrients and COD concentration.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.379-397
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• 2020

In this study, Long-term change of water quality in Donrae Hot Spring was characterized using water quality data from 1922 to 2019. According to Mann-Kendall analysis and Sen's slope using long-term water quality data of Dongnae Hot Spring from 1922 to 2019, temperature, Ca, SiO₂, and HCO₃ show an increasing trend whereas EC, Na, K, Mg, Cl, and SO₄ show a decreasing trend or negligible trend. In addition, the water type of Dongnae Hot Spring stably belongs to Na-Cl type over time. The spatial distributions of water temperature and chemical constituents in 2004, 2009, 2014, and 2019 show variable patterns with showing some difference depending on sampling locations in different years. These results indicate that despite the long-term pumping of the hot spring water, the water quality is quite stable during the entire study period.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.707-720
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• 2011

For the lake case, the detention phenomenon of water body occurs and stays for a long time. Especially, following the layer of water depth direction, the lake body and water quality problems are different from the water quality of river. So according to time, the stream and water quality can be simulated by the 3-Dimensional Model, which can divide water layer for reservoir or lake. The water quality simulation result will become more reliability. For this study, the 3-Dimension Model - EFDC was used to simulate water quality of Unam reservoir in the Sumjin Dam. The HEC-GeoHMS and HEC-HMS Rainfall - Runoff Model based on GIS were used to estimate long-term runoff, and input data was constructed to the observed water level, meteorological data, water temperature, T-N and T-P. In order to apply the EFDC model, water depth was divided into 3 layers and 5,634 grids were extracted. After constructing the grid net, the water quality change of Unam reservoir in time and space was simulated. Overall, long term runoff simulation reflected the actual observed runoff well, through the water quality simulation, according to the pollution factors, the behavior characteristics can be checked, and the simulated water quality can be properly reflected. The function of EFDC has been confirmed, which water quality can be properly simulated. In the near future, to establish countermeasures for Intake Facilities of Watershed and Management, this support which some basic tools can be applied is in expectation.

• Journal of Wetlands Research
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• v.24 no.4
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• pp.256-267
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• 2022

This study analyzed the current status and trend of water quality using long-term water quality monitoring data measured over the past 5 years in the Seohwacheon Basin, located upstream of Daecheong Lake. In the Seohwacheon Basin, a project is underway to reduce the occurrence of algal blooms in the Daecheong Lake and to improve water quality, and continuous management is required for water quality management. The current water quality evaluation aims to identify the water quality management point, and the good water grade and the integrated water quality index (WQI) were used. For trend evaluation, the effect of the water quality improvement project was evaluated using the Mann-Kendall test and Sen's Slope. As a result of the evaluation, the current water quality index was used to identify the watersheds and when to manage water quality, and the effect of the improvement project was confirmed through trend analysis. Through this study, it is possible to review the water quality status and improvement effect using long-term water quality monitoring data, so it is expected to be applicable to similar types of watersheds in the future.

• Journal of Environmental Science International
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• v.16 no.12
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• pp.1411-1424
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• 2007

Long-term trends and distribution patterns of water quality were investigated in the Asan coastal areas of Yellow Sea, Korea from 1975 to 2005. Water samples were collected at 3 stations and physicochemical parameters were analyzed including water temperature, salinity, suspended solids(SS), chemical oxygen demand(COD), dissolved oxygen(DO) and nutrients. Spatial distribution patterns were not clear among stations but the seasonal variations were distinct except COD, SS and nitrate. The trend analysis by principal component analysis(PCA) during twenty years revealed the significant variations in water quality in the study area, Annual water qualities were clearly discriminated into 4 clusters by PCA; year cluster 1988-1991, 1994-1997, and 1992-1993/1998-2005. By this multi-variate analysis we can summarize the annual trends as the followings; salinity, suspended solids and dissolved oxygen tended to increase from late 1980's, increased pH and COD from 1992, and decreased salinity and increased nitrogen and COD from 1990 due to the runoff frow agricultural lands causing eutrophication.

• Journal of Environmental Science International
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• v.13 no.11
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• pp.993-1000
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• 2004

As an effort to clarify the ecosystem of Osaka Bay, a semi-enclosed coastal area under the influence of stratification, a three-dimensional water quality model with combination of the baroclinic flow model and primitive eco-system model was constructed. The proposed model succeeded in simulating the time-depending flow and density structure and the baroclinic residual currents in Osaka Bay. In present study, we tried to improve the model by taking account of the benthic-pelagic interaction and exchange of nutrients between sea bottom sediments and overlaying water. On vertical structure, the model consists of 13 layers of water and eight layers of sediments. Long-term prediction of water quality was conducted from 1964 to 1985. This period is characterized by rapid water pollution and its decrease by the cutoff reduction of COD and P flowed into Osaka Bay. By combining the sediment model into original model, the numerical model was confirmed to shows more reasonable results in simulating the water quality in Osaka Bay.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.4
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• pp.263-267
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• 2013

Long-term (from 1995 to 2012) data of phytoplankton biomass (chlorophyll-a, Chl-a) and water quality were analyzed to investigate trends of eutrophication in downstream of Nakdong River (Mulgum). Long-term annual average concentration of water quality parameters and phytoplankton biomass at Mulgum showed an decreasing trends for 18 years. Phytoplankton biomass was high from annually December to March. Trophic state was evaluated as the eutrophic state annually from 1995 to 2012 by TSI (trophic state index) by Aizaki. From the results of simple regression analysis, correlation coefficient between Chl-a concentration and BOD concentration was high ($r^2$ = 0.82).