• Journal of Korea Water Resources Association
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• v.35 no.2
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• pp.137-148
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• 2002

The purpose of this study was to examine the effect of water quality improvement due to dredging the bottom deposit at the downstream of a urban river. The finite difference method was used to analyze the water quality variations caused by the depths of dredging and intercepting ratios of the goal years. 21 boring points were selected along the 11.2km river reach running through a metropolitan city. The pollution levels of the deposits from the bored Points were examined by the leaching test. The improvement effect of the water quality, measured as changes of COD, were carried at under drought, minimal, and normal flow. The result indicates that the dredging of the contaminated sludge contributes the improvement of the water quality.

• Journal of Environmental Science International
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• v.20 no.5
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• pp.589-598
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• 2011

Milyanggang mid-watershed is located in downstream of Nakdong river basin. The pollutants from that watershed have an direct effect on Nakdong river water quality and it's control is important to manage a water quality of Nakdong river. A target year of Milyanggang mid-watershed water environment management plan is 2013. To predict a water quality at downstream of Milyang river, we have investigated and forecasted the pollutant source and it's loading. There are some plan to construction the sewage treatment plants to improve the water quality of Milyang river. Those are considered on predicting water quality. As results, it is shown that the population of Milyanggang mid-watershed is 131,857 and sewerage supply rate is 62.2% and the livestock is 1,775.300 in 2006. It is estimated that the population is 123,921, the sewerage supply rate is 75.5% in 2013. The generated loading of BOD and TP is 40,735 kg/day and 2,872 kg/day in 2006 and discharged loading is 11,818 kg/day and 722 kg/day in 2006 respectively. Discharged loadings were forecasted upward 1.0% of BOD and downward 2.7% of TP by 2013. The results of water quality prediction of Milyanggang 3 site were 1.6 mg/L of BOD and 0.120 mg/L of TP in 2013. It is over the target water quality at that site in 2015 about 6.7% and 20.0% respectively. Consequently, there need another counterplan to reduce the pollutants in that mid-watershed by 2015.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.155-162
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• 2010

Recently, due to high oil prices and environmental pollution issues, interest of alternative energy development increases and the related research is widely conducted. Among those research activities the tidal stream power generation utilizes the tidal flow as its mechanical power resource and less depends on the environmental condition for installation and operation than other renewable energy resources. Therefore the amount of power generated is quite consistent and straightforward to predict. However, research on the tidal stream energy conversion turbine is rarely found. In the present study, two numerical methods were developed and compared for the open water Momentum Theory, which is widely used for wind turbines, was adopted. The moving reference frame method for Computational Fluid Dynamis solver were also used. Hybrid meshing was used for the complex geometry of turbines. The analysis results using each method were compared to figure out a better method for the performance prediction.

• Korean Journal of Environmental Agriculture
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• v.11 no.1
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• pp.20-25
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• 1992

To understand the fundamental features of reservoir environment and its future aspects, a simple predictive model for water quality was attempted with the aid of data easily obtained, Based on the data from 12 reservoirs in Korea, application of the simple predictive model was successfully made by means of statistical methods and simple physical submodels. Significant information on th effects of retention time on primary production in a reservoir, longitudinal change in water quality affected by certain non-dimensional parameters were also obtained. The chlorophyll-a concentration can be predicted by the equation as ; chlorophyll-a=($395{\time}limiting$ nutrient concentration) - 1.090.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.1
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• pp.37-45
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• 2011

Mandae watershed in Gangwon province has been known as one of soil erosion hot spot watersheds within Hanggang basin. Thus numerous efforts have been made to reduce soil erosion and pollutant loads into receiving watershed. However, proper best management practices have not been suggested because no monitoring flow and water quality data were available. Thus, modeling technique could not be utilized to evaluate water quality issue properly at Mandae watershed to develop and implement the best management practices. In this study, the SWAT model was applied to the Mandae watershed, Gangwon province to evaluate the SWAT prediction ability and water quality improvement with vegetated filter strip (VFS) in this study. The Nash-Sutcliffe model efficiency (NSE) and Coefficient of determination ($R^2$) values for flow simulation were 0.715 and 0.802, respectively, and the NSE and $R^2$ values were 0.903 and 0.920 for T-P simulation indicating the SWAT can be used to simulate flow and T-P with acceptable accuracies. The SWAT model, calibrated for flow and T-P, was used to evaluate water quality improvement with the VFS in agricultural fields. It was found that approximately 56.19 % of T-P could be reduced with vegetated filter strip of 5 m at the edge of agricultural fields within the watershed (34.86 % reduction with VFS of 1m, 48.29 % with VFS of 3 m). As shown in this study, the T-P, which plays key roles in eutrophication in the waterbodies, can be reduced with proper installation of the VFS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.57-64
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• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.611-617
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• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.

• Environmental Engineering Research
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• v.15 no.2
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• pp.123-126
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• 2010

Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales ($1.5\;{\times}\;10^5$ and $1.62\;{\times}\;10^6\;m^2$) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfall-surface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.

• Journal of Ocean Engineering and Technology
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• v.15 no.4
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• pp.53-59
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• 2001

The Primary Production of phytoplanktons produces organic matter in high concentration in eutrophicated Hakata Bay, Japan, even during the winter season in spite of low water temperature. Phytoplanktons are considered to have any biological capabilities to keep activities of photosynthesis under the unfavorable conditions, and this affects water quality of the bay. In this study, seasonal variations in primary production efficiency were predicted by using a simple box-type ecosystem model, which introduced the concept of efficiency for absorption of solar radiation energy in relation to growth of phytoplanktons under the low solar radiation intensity. According to the simulation result of primary production, it was organic pollution comes from dissolved organic carbon (DOC) throughout the year, DOC of which is originated from the primary production of phytoplanktons on biological response of the seasonal variation of ambient conditions.