• 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 Industrial Technology
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• v.27 no.B
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• pp.221-227
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• 2007

This research investigates the turbidity and flow of river for an year, 2005, in order to examine the relation of the generation of turbidity according to the outflow of min water. For this research, the region of Jawoon river where is the area of high land vegetable growing in the upper Soyang Reservoir is selected to observe actual floating materials that generate negative nutrition and turbidity of the Reservoir water and the changes of water quality by raining of each month. In addition, the researcher has conducted statistical inspection methods, such as correlation analysis and regression analysis on strength of raining force, and rain continuance time among the elements affecting the outflow of floating materials.

• 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

• 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.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.197-203
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• 2005

Dredging and reclaiming on coast, harbor construction etc. of when construct, the interest about efficiency and accuracy of sounding by measurement condition very rise. However, there are only a few studies on the accuracy improvement concerning water depth sounding condition. In this study, among the precision decline main causes of sounding, 1 suggested the characteristics of sounding data acquired by echo sounder with increasing of turbidity and the critical turbidity range under a given transducer frequency. 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. When the turbidity exceeds $217\~259$ NTU which was considered as critical range, depth sounding was impossible.

• 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.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.101-111
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• 2022

This study analyzed the relationship between water turbidity and its reflectance, as measured using hyperspectral imaging. First, samples of turbid water were generated in boxes. This was followed by drone-based hyperspectral imaging and analysis of the correlation between the samples' measured turbidity and hyperspectral reflectance. The nine boxes for turbidity measurement were made of black acrylic that absorbed all light turbidity was induced using soil collected near Changhacheon, which causes turbidity in Imha Lake. The results indicate that the reflectance of wavelengths in the near-infrared region followed a pattern of increase with increasing soil content for each box. Analysis of this correlation between the turbidity and average reflectance measured in each box yielded a very high R² value of 0.8702, indicating that reflectance is a suitable proxy for turbidity.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.1-9
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• 2016

A correlation analysis between raw water turbidity at two wide-area water treatment plants and hydrological data was conducted for efficient water supply, design and management of water treatment plant. Both correlation analysis and principal component analysis were conducted using hydrological time series data such as inflow discharge, outflow discharge, and rainfall at dam basin of intake station of wide-area water treatment plants. And, forecasting of change in turbidity was conducted using regression equation for turbidity prediction. The raw water turbidity of two water treatment plants was strongly related to time series of discharge. The raw water turbidity of Chungju water treatment plant is strongly related to outflow discharge at Chungju dam (0.708). Whereas, the raw water turbidity of Wabu water treatment plant is strongly related to inflow discharge at Paldang dam (0.805). Similar trends between turbidity forecasting result using regression equation and calculation result using estimation equation on Korea water supply facilities standard were obtained. The result of this study can provide basic data for construction and management of water treatment plant.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.3
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• pp.123-128
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• 2008

This study inquired into changes of turbidity of a river depending on the rainfall characteristics of each month through investigating the water quantity and turbidity of Jawoonchun drainage basin in the upper reaches of Soyangho (lake) for the year of 2005. It could be seen from the study that high turbidity occurred during the period from May to August due to rainfall intensity and rainfall duration. On the other hand, it could be confirmed that turbidity was constantly low after September because of early cleansing effect of rainfall. Next, the influence of controlling factors of turbidity was looked into. For May and June, it was 89% rainfall intensity among the controlling factors, for July and August, it was 67% rainfall intensity and 32% rainfall duration that was confirmed to have influence over the occurrence of turbidity. In addition, regression analysis was carried out about the controlling factors that had influence over the occurrence of turbidity and regression equation was suggested.