• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.451-454
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• 2004

The study aims to assess the quality of bank filtrate in relation to streamflow and physico-chemical properties of the stream. Turbidity, pH, temperature and dissolved oxygen (DO) of Nakdong River and riverbank filtrate were statistically analyzed. The physico-chemical properties of riverbank filtrate were measured from irregularly different seven pumping wells every day. Autocorrelation analyses were conducted to the qualities of stream water and bank filtrated water. Temperature, pH and DO of streamflow shows strong linearity and long memory effect, indicating the effect of seasonal air temperature and rainy season. Temperature of riverbank filtrate shows weak linearity and weak memory, indicating differently from the trend of stream temperature. Turbidity of steramflow shows strong linearity and long memory effect, while turbidity of riverbank filtrate indicates weak linearity and weak memory. Cross-correlation analysis shows low relation between turbidity, pH, temperature and DO of riverbank filtrate and those of streamflow. Turbidity of streamflow was largely affected by the streamflow rate, showing a similar trend with autocorrelation function of streamflow rate. The turbidity of riverbank filtrate has a lag time of 25 hours. This indicates that turbidity of streamflow in a dry season has very low effect on the turbidity of riverbank filtrate, and a high turbidity of the stream in a rainy season has a fairly low effect on the turbidity of riverbank filtrate.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1184-1188
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• 2005

The impacts of turbidity flow induced by summer rainfall events on water supply, aquatic ecosystems, and socioeconomics are significant and major concerns in most of reservoirs operations. As a decision support tool, the real-time turbidity flow monitoring and modeling system RTMMS is under development using a laterally integrated two-dimensional (2D) hydrodynamic and water quality model. The objectives of this paper is to present the preliminary field observation results on the characteristics of rainfall-induced turbidity flows and their density flow regimes, and the model performance in replicating the fate and transport of turbidity plume in a reservoir. The rainfall-induced turbidity flows caused significant drop of river water temperature by 5 to $10^{\circ}C$ and resulted in density differences of 1.2 to $2.6kg/m^3$ between inflow water and ambient reservoir water, which consequently led development of density flows such as plunge flow and interflow in the reservoir. The 2D model was set up for the reservoir. and applied to simulate the temperature stratification, density flow regimes, and temporal and spatial turbidity distributions during flood season of 2004 After intensive refinements on grid resolutions , the model showed efficient and satisfactory performance in simulating the observed reservoir thermal stratification and turbidity profiles that all are essentially required to enhance the performance of RTMMS.

• The KSFM Journal of Fluid Machinery
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• v.10 no.6
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• pp.50-56
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• 2007

In this study, we have performed some experimental works on the effects of variation of low, middle and high turbidity for understanding of optical characteristics which is very important factor for the turbidity measurement. The various output frequencies were obtained by the experimental apparatus which consist of detectors, a light source, a frequency counter and so on. From the result of analysis of these frequencies, Firstly, The difference of signal value for each degrees of low turbidity was the smallest of three scopes around the Nephelometric position. Second, the characteristics of each degrees of middle turbidity was proved that signal values of all degrees were larger those of low turbidity but the difference of each signal value of the forward direction was smaller than that of the backward direction. Third, the characteristics of each degrees of high turbidity was proved that though similar to the characteristics of middle turbidity, each signal value of all degrees was larger and the difference of each signal value of all degrees was smaller than those of low and middle turbidity

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.5
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• pp.659-668
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• 2012

In Korea, almost every water treatment plant suffers from seasonal problem of algae and turbidity which result from eutrophication and heavy rainfall. To relieve this problem, experimental investigation was performed to study the applicability of a floating-media and sand filter to preliminary water treatment in terms of algae and turbidity removal. Experimental results using pure-cultured algae influent showed that the shape of algae species as well as filtration velocity affects the removal efficiency. From the experiments using natural river water, it was concluded that algae removal is more sensitive to floating-media depth but turbidity more sensitive to sand depth. As the filtration velocity increased, the removal of turbidity decreased but that of algae was not affected. The floating-media and sand filter removed more than 30 % of TP, TN, turbidity, Chl-a and CODcr, and less than 20 % of DOC and $UV_{254}$.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.40 no.4
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• pp.10-15
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• 2008

Flocculation phenomena of the stock mixed with cellulosic fibers, fillers and polymers were investigated by a new turbidity measurement system consisted of a probe-type turbidimeter, data acquisition system and computer. The probe-type turbidimeter allowed to measure the real time flocculation of the stock induced by single polymer and microparticle systems. Flocculation phenomena were evaluated by average and final relative turbidity indices. Turbidity and flocculation showed inverse relationship, i.e. the turbidity decreased with the formation of flocs. Relative turbidity of the stock treated with microparticle system was lower than that of the stock containing single polymer system, which indicated that the microparticle system showed greater floc forming efficiency than single polymer system.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.148-149
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• 2005

In this study, among the precision decline main causes of sounding, I suggested the characteristics of sounding data acquired by echo sounder with increasing of turbidity For this, I acquired sounding data by inputting turbidity inducer artificially in artificial water tank. And then achieved regression analysis. Conclusion are as following : Sounding Capabilities can be divided into three ranges according to the turbidity : normal range, critical range and the range where data can not be obtained by an echo sounder

• Applied Chemistry for Engineering
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• v.20 no.6
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• pp.681-684
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• 2009

The turbidity of flow type samples has a nonlinear relation to brightness of laser scattered light, but the shape of images in laser scattered light is different from each turbidity samples. The turbidity measurement will be easy if it uses a pattern of images in laser scattered light. But the excessive analysis load comes from the turbidity measured by red, green, blue intensity (intensity) of all pixels of images in laser scattered light. Therefore the images in laser scattered light were divided by appropriate block to decrease excessive analysis load. The shape of divided images in laser scattered light was different from each turbidity sample. The real turbidity has a linear relation to turbidity measured by the artificial neural network learned with the intensity of divided images in laser scattered light and turbidity.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.125-134
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• 2023

In order to develop a backtracking technique for turbidity measurement data without discriminatory characteristics, three turbidity backtracking techniques for predicting inflow turbidity of a stream were compared using real-time turbidity data measured at automatic water quality measurement points located upstream and downstream of the stream and the Py_STPS model. Three turbidity backtracking techniques were applied: 1) simple preservation method of turbidity load considering flow time, 2) a method of using the flow rate at the upstream boundary considering the flow time as the flow rate at the downstream boundary, 3) method of introducing internal reaction rate to reflect the behavior characteristics of turbidity-causing substances. As a result of applying the three backtracking models, it was confirmed that the backtracking technique that introduced the internal reaction rate had the best results.