• KCID journal
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• v.1 no.1
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• pp.14-24
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• 1994

In this study, the Water Quality Management Model for Rural Areas(WQMMRA) has been developed by combining the Model for Pollutant Load Computation(MPLC) and the existing QUAL2E model. The WQMMRA model was applied to evaluate its applicability for the repr

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.73-85
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• 2024

Predicting water quality of rivers and reservoirs is necessary for the management of water resources. Artificial Neural Networks (ANNs) have been used in many studies to predict water quality with high accuracy. Previous studies have used Gradient Descent (GD)-based optimizers as an optimizer, an operator of ANN that searches parameters. However, GD-based optimizers have the disadvantages of the possibility of local optimal convergence and absence of a solution storage and comparison structure. This study developed improved optimizers to overcome the disadvantages of GD-based optimizers. Proposed optimizers are optimizers that combine adaptive moments (Adam) and Nesterov-accelerated adaptive moments (Nadam), which have low learning errors among GD-based optimizers, with Harmony Search (HS) or Novel Self-adaptive Harmony Search (NSHS). To evaluate the performance of Long Short-Term Memory (LSTM) using improved optimizers, the water quality data from the Dasan water quality monitoring station were used for training and prediction. Comparing the learning results, Mean Squared Error (MSE) of LSTM using Nadam combined with NSHS (NadamNSHS) was the lowest at 0.002921. In addition, the prediction rankings according to MSE and R² for the four water quality indices for each optimizer were compared. Comparing the average of ranking for each optimizer, it was confirmed that LSTM using NadamNSHS was the highest at 2.25.

• Journal of Environmental Impact Assessment
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• v.4 no.3
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• pp.31-39
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• 1995

Environmental Impact Assessment (EIA) is composed of various procedures, such as screening, scoping, inventory survey, prediction, assessment, alternative assessment, mitigation measures, and post management. Environmental monitoring data for air quality or water quality, etc. is applied in the EIA process, especially in prediction and post management. As an effective tool of environmental monitoring, the remote sensing method, introduced recently, was used in collecting nationwide data concerning ecosystem and land use. This article explains the current monitoring status in Korea. Monitoring factors include air quality, water quality, soil, ocean, odor, noise, and ecosystems. This report explains the organization of the environmental monitoring system managed by the Ministry of Environment in Korea. Furthermore, it shows the environmental criteria and environmental policies applied to EIA in Korea.

• Journal of Environmental Impact Assessment
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• v.10 no.3
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• pp.235-243
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• 2001

Since the middle of 1990s, in Korea a few researches on the optimal management technologies combining numerical model and GIS for the management of water environment in drinking watershed area and reservoir such as Paldang Lake have been carried out. In this study, the integrated water environment management system was been suggested to efficiently reflect the public awareness of the environment by integrating the web based distributed data collection system, GIS, public hearing system and water quality model. As all the components of the system have been developed using the World Wide Web and all data have been collected from the relevant agencies through the Internet, the water quality model could be implemented on the web directly. In consequence, the environmental geographic information in Paldang Lake could be acquired and analyzed through the Internet. The system can rapidly respond to the public right to know on environment, so the public will willingly participate in the governmental projects on environment. To verify the usability of the developed system, it has been applied to Paldang Lake. Especially when the web based model has been used, users can easily and confidentially get the prediction results by applying the minimum number of parameters for the water quality model. This model will provide clearness and scientific bases in the process of water quality prediction for the sensitive sites where there are critical conflicts between the residents and the developers. In this study, rapid water environment management technique without spatial and time limit has been suggested, which can contribute to the efforts on the government and the public participation.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.24-35
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• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.5
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• pp.373-386
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• 2023

To analyze the prediction of geological conditions and water-bearing zones, TSP was performed in the collapse zone of the fault zone. The results of the TSP were verified by comparing them to the face mapping results of the prediction zone. The rock quality prediction result of the TSP had an error of about 3 to 10 meters compared to the face mapping result, but the overall rock quality change and ground condition were analyzed to be relatively similar. In the water-bearing zones of the face mapping results, the Vp/Vs ratio ranges from 1.79 to 2.37 and the Poisson's ratio ranges from 0.27 to 0.39. In the sections other than the water-bearing zones, the Vp/Vs ratio ranges from 1.61 to 1.89, and the Poisson's ratio ranges from 0.19 to 0.3. As a result of analyzing the Vp/Vs ratio and Poisson's ratio in the water-bearing zones, it is analyzed that the sections with a Vp/Vs ratio of 2.0 or more and a Poisson's ratio of 0.3 or more have a high possibility of being water-bearing zones.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.307-325
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• 2023

Due to the prolonged COVID-19 pandemic, the frequency of people who are tired of living indoors visiting nearby mountains and national parks to relieve depression and lethargy has exploded. There is a place where thousands of people who came out of nature stop walking and breathe and rest, that is the mineral spring. Even in mountains or national parks, there are about 600 mineral springs that can be found occasionally in neighboring parks or trails in the metropolitan area. However, due to irregular and manual water quality tests, people drink mineral water without knowing the test results in real time. Therefore, in this study, we intend to develop a model that can predict the quality of the spring water in real time by exploring the factors affecting the quality of the spring water and collecting data scattered in various places. After limiting the regions to Seoul and Gyeonggi-do due to the limitations of data collection, we obtained data on water quality tests from 2015 to 2020 for about 300 mineral springs in 18 cities where data management is well performed. A total of 10 factors were finally selected after two rounds of review among various factors that are considered to affect the suitability of the mineral spring water quality. Using AutoML, an automated machine learning technology that has recently been attracting attention, we derived the top 5 models based on prediction performance among about 20 machine learning methods. Among them, the catboost model has the highest performance with a prediction classification accuracy of 75.26%. In addition, as a result of examining the absolute influence of the variables used in the analysis through the SHAP method on the prediction, the most important factor was whether or not a water quality test was judged nonconforming in the previous water quality test. It was confirmed that the temperature on the day of the inspection and the altitude of the mineral spring had an influence on whether the water quality was unsuitable.

• Food Science and Preservation
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• v.30 no.2
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• pp.224-234
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• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• 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 environmental and Sanitary engineering
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• v.15 no.3
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• pp.101-119
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• 2000

In order to establish water quality management planning in some watershed, water quality of the future of the watershed should be predicted first. The Yongsan river various pollutant sources ; sewage, industry, livestock, farming and so on. And pollutants from these sources are likely to increase even though a number of publicly owned treatment works(POTWs) are founded. Therefore, it is estimated that water quality if the river would be even worse than now in near future. In this study, water quality of the future(2001, 2006) on the Yongsan river was simulated with QUAL2E model. Concentration of three water quality parameters(BOD, T-N, T-P) was predicted according to dry season, low flow season, average flow season of the river with and without POTWs. The results of this study showed the significant contrast in concentration between with and without POTWs, specially in terms of T-N and T-P. Therefore, POTWs must be founded around the Yongsan river and more advanced treatment should be considered. And because these parameters are mostly affected by polluants from upper watershed, including Kwangiudcheon, water quality management planning on the Yongsan river might be focused on this area.