• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.65-76
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• 2012

Agricultural reservoir water quality simulation model (ARSIM-rev) was developed in this study for water quality simulation of a small and shallow agricultural reservoir with limited observed water quality data. Developed ARSIM-rev is a zero-dimensional water quality model because of little spatial differences in water quality between stations in a small and shallow agricultural reservoir. ARSIM-rev used same water quality reaction equations with WASP except for several equations, and daily based input parameters such as settling rate, release rate from sediment, and light extinction coefficient changed yearly based input parameters in ARSIM-rev. A number of pre- and post-processors were developed such as auto calibration and scenario analysis for ARSIM-rev. CE-QUAL-W2, WASP, and developed ARSIM-rev were applied to Mansu agricultural reservoir to evaluate model performance, and ARSIM-rev demonstrated similar model performance with CE-QUAL-W2 and WASP when low number of observed data was used for agricultural reservoir water quality simulation. Overall, developed ARSIM-rev was feasible for water quality simulation in a small and shallow agricultural reservoir with limited observed water quality data, and it can simulate agricultural reservoir water quality precisely enough like common water quality model such as CE-QUAL-W2 and WASP within a limited time.

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.294-312
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• 2010

Data Technology (DT) is a new technology which deals with data collection, data analysis, information generation from data, knowledge generation from modelling and future prediction. DT is a newly emerged interdisciplinary science & technology in this 21st century knowledge society. Even though the main body of DT is applied statistics, it also contains management information system (MIS), quality management, process system analysis and so on. Therefore, it is an interdisciplinary science and technology of statistics, management science, industrial engineering, computer science and social science. In this paper, first of all, the definition of DT is given, and then the effects and the basic properties of DT, the differences between IT and DT, the 6 step process for DT application, and a DT example are provided. Finally, the relationship among DT, e-Statistics and Data Mining is explained, and the direction of DT development is proposed.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.430-436
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• 2015

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

• Journal of Korean Library and Information Science Society
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• v.54 no.3
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• pp.105-128
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• 2023

In 2020, the Busan metropolitan library took the lead in establishing an integrated library system (ILS) that integrates bibliographic data from 49 public libraries and 103 small public libraries. However, each library still builds bibliographic data individually and repeatedly, and the bibliographic data built by each library is only physically stored in an integrated DB. Therefore the improvement in work efficiency or data quality has not been achieved. This study aimed to analyze the construction processes and quality of bibliographic data in Busan public libraries and to suggest a new implementation strategy for an integrated environment. To this end, (1) the construction process of bibliographic data was investigated, (2) the quality of the constructed bibliographic data was objectively analyzed, and (3) four implementation strategies were suggested based on critical problems. The implementation strategy aims not only to improve the quality of bibliographic data, but also to increase work efficiency and build an infrastructure for data sharing.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.349-357
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• 2005

Principal component analysis(PCA) was carried out to evaluate the water quality with the monitering data collected from 1997 to 2003 along the coastal area of Ulsan, Korea. To enhance evaluation and to complement descriptive power of traditional PCA, optimal scaling was applied to transform the original data into optimally scaled data. Cluster analysis was also applied to classify the monitering stations according to their characteristics of water quality.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.233-240
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• 2018

In this study, variables were constructed based on prior research to examine the impact of information system quality (information quality, system quality, service quality), customer value and satisfaction in hotel comparison site. The samples consist of 205 survey data drawn from hotel comparison site users. The collected data were analyzed by SPSS 24.0 and AMOS 21.0. According to results of the reliability and validity test, all were found reliable, and all items were included. The results are as follows: first, Among the information system quality (information quality, system quality, and service quality) of the hotel comparison site, the information quality and service quality have positive effects on customer value. Second, the information system quality(information quality, system quality, and service quality) of the hotel comparison site has a positive effect on customer satisfaction. Third, the customer value of the hotel comparison site has a positive effect on customer satisfaction.

• Journal of Environmental Impact Assessment
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• v.2 no.2
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• pp.73-83
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• 1993

Increasing concern for the environment in Korea has led to the demand that major policies and large-scale development projects be subjected to detailed impact assessment. This paper reports on the state of data related to the prediction of the environmental impact (EIA) to emphasize the importance of data quality. Environmental impact statements (EIS) consulted with the Ministry of Environment of Korea were analyzed from 1981 through 1992. Many of assessors used existing data and collected supplementary data from field survey. Most of the results of EIA are presented directly or summarized on maps and as graphics. For the national purpose, large source of quality-controlled data such as atmospheric data have been developed, However, there are the deficiency in data to analyze the impact of human activity, and data gaps and incompatibilities among systems. Consequently, the development of data bank systems including computer database and remotely-sensed satellite data is required to improve the quality of data which are relevant to EIA. The data bank system should be organized meaningfully in minimum time with a least cost, and measurement standards must be made explicit. Geographical information systems (GIS) are applicable to the graphic presentation or to the impact prediction model.

• Journal of Korea Water Resources Association
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• v.44 no.6
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• pp.439-447
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• 2011

This study aims to test the feasibility of combined use of EFDC (Environmental Fluid Dynamics Code) hydrodynamic model and WASP7.3 (Water Quality Analysis Program) model to improve accuracy of water quality predictions of the Yongdam Lake, Korea. The orthogonal curvilinear grid system was used for EFDC model to represent riverine shape of the study area. Relationship between volume, surface and elevation results were checked to verify if the grid system represents morphology of the lake properly. Monthly average boundary water quality conditions were estimated using the monthly monitored water quality data from Korean Ministry of Environment DB system. Monthly tributary flow rates were back-routed using dam discharge data and allocated in proportion to each basin area as direct measurements were not available. The optimum number of grid system was determined to be 372 horizontal cells and 10 vertical layers of the site for 1 year simulation of hydrodynamics and water quality out of iterative trials. Monthly observed BOD, TN, TP and Chl-a concentrations inside the lake were used for calibration of WASP7.3 model. This study shows that EFDC and WASP can be used in series successfully to improve accuracy in water quality modeling. However, it was observed that the amount of data to develop inflow water quality and flow rate boundary conditions and water quality data inside lake for calibration were not enough for accurate modeling. It is suggested that object-oriented data collection systems would be necessary to ensure accuracy of EFDC-WASP model application and thus for efficient lake water quality management strategy development.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.315-325
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• 2016

Due to the expansion of data and technical progress in the military industry, it is important to extract meaningful information for assuring quality and making policies. The analysis of trends and decision making based on big data is helpful for increasing productivity in business and finding new business opportunities. We propose an application to collect reliable quality information for munitions and build a big data platform for using the accumulated information and numerical data. We verified the proposed platform using the Test Report Information Service (TRIS) system and suggest a method that utilizes unstructured and semi-structured data accumulated by TRIS. Thus, we expect that the proposed platform will help in building infrastructure for military data, making efficient strategies, and analyzing trends for assuring munitions quality.