• Korean Journal of Ecology and Environment
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• v.43 no.2
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• pp.307-318
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• 2010

This study was to analyze the ecological conditions, based on physical habitat, chemical, and biological conditions before (2006, 2007) and after ecological restoration (2009) in five sites of Changwon Stream (CS) and six sites of Nam Stream (NS), respectively, and then to compare ecological health between the two period. The analysis of ecological health was based on the multimetric models of Index of Biological Integrity (IBI) and Qualitative Habitat Evaluation Index (QHEI) along with water chemistry in the streams. For the study, the models of IBI and QHEI were modified as 8 and 11 metric attributes, respectively. For the evaluations, the survey was conducted in the period of 2006~2007 before the restoration and in 2009 after the restoration by the city. Chemical conditions, based on conductivity, in both streams showed a typical longitudinal declines along the axis of the upstream-to-downstream. There were no significant differences (p>0.05) in water quality between the two periods. Values of IBI in the CS and NS averaged 21.6 and 19.7, respectively, indicating a C grade in the criteria of Ministry of Environment, Korea, and there was no significant differences in the IBI between the two periods. Values of QHEI after the restoration averaged 29.2 and 63.2 in the CS and NS, respectively and the values decreased markedly especially, in the NS (35.3) after the restoration. The habitat disturbance was mainly attributed to destructions (i.e., the narrower width of riparian vegetation and higher substrate exposure by the air) of artificial materials by massive flood in 2009. Overall, our results suggest that the restoration was not effective in the two streams between the two periods, even if the budget was used a lot and that such ecological restoration, not considered the natural disaster, may not effect for the stream restoration.

• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.90-96
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• 2013

This study was conducted to investigate a stability trend within 6 above average and 4 blow average erosion control dams, which were selected by The Korean Association of Soil and Water Conservation and were built in 1990s in Gyeonggi and Gangwon Province. The study was aimed to measure rebound hardness of upstream face, flood way and downstream face from those dams selected by using 'Concrete Test Hammer'. The main purposes of the study are selection of compression strength prediction equation and scope of wavelength, which successfully demonstrate non-destructive test results for erosion control dams. There is an opportunity to increase disaster prevention ability when stability vulnerability of concrete erosion control dam is detected in a timely manner. Results of the compression strength investigation express that there is a consistency with visual inspection of stability that has been processed by The Korean Association of Soil and Water Conservation. We concluded that a prediction equation, which was developed by Architectural Institute of Japan (AIJ), shows highest suitability in Korean erosion control dams when stability investigation is performed. The detailed criteria for the test result are 'stable', 'detail inspection required' and 'poor' for over 300 $kgf/cm^2$, 250~300 $kgf/cm^2$ and below 250 $kgf/cm^2$ respectively. Standards for stability of Korean erosion control dam and a compression strength prediction equation (that corresponds to the standards of the stability) should be established on the basis of chronological data of erosion control dam compression strength. Systematical approach for stability inspection that carries out remodeling or repair when problem on erosion control structures are detected through visual inspection and simple stability test, is necessary for the future disaster prevention.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.81-94
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• 2006

The water quality impacts of forest fragmentation at the Doam-dam watershed were evaluated in this study. For this ends, the watershed scale model, Soil and Water Assessment Tool (SWAT) model was utilized. To exclude the effects of different magnitude and patterns in weather, the same weather data of 1985 was used because of significant differences in precipitation in year 1985 and 2000. The water quality impacts of forest fragmentation were analyzed temporarily and spatially because of its nature. The flow rates for Winter and Spring has increased with forest fragmentations by $8,366m^3/month$ and $72,763m^3/month$ in the S1 subwatershed, experiencing the most forest fragmentation within the Doam-dam watershed. For Summer and Fall, the flow rate has increased by $149,901m^3/month$ and $107,109m^3/month$, respectively. It is believed that increased flow rates contributed significant amounts of soil erosion and diffused nonpoint source pollutants into the receiving water bodies. With the forest fragmentation in the S1 watershed, the average sediment concentration values for Winter and Spring increased by 5.448mg/L and 13.354mg/L, respectively. It is believed that the agricultural area, which were forest before the forest fragmentation, are responsible for increased soil erosion and sediment yield during the spring thaw and snow melts. For Spring and Fall, the sediment concentration values increased by 20.680mg/L and 24.680mg/L, respectively. Compared with Winter and Spring, the increased precipitation during Summer and Fall contributed more soil erosion and increased sediment concentration value in the stream. Based on the results obtained from the analysis performed in this study, the stream flow and sediment concentration values has increased with forest fragmentation within the S1 subwatershed. These increased flow and soil erosion could contribute the eutrophication in the receiving water bodies. This results show that natural functionalities of the forest, such as flood control, soil erosion protection, and water quality improvement, can be easily lost with on-going forest fragmentation within the watershed. Thus, the minimize the negative impacts of forest fragmentation, comprehensive land use planning at watershed scale needs to be developed and implemented based on the results obtained in this research.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.53-69
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• 2020

The increase of the impermeable area due to industrialization and urban development distorts the hydrological circulation system and cause serious stream drying phenomena. In order to manage this, it is necessary to develop a technology for impact assessment of stream drying phenomena, which enables quantitative evaluation and prediction. In this study, the cause of streamflow reduction was assessed for dam and weir watersheds in the five major river basins of South Korea by using distributed hydrological model DrySAT-WFT (Drying Stream Assessment Tool and Water Flow Tracking) and GIS time series data. For the modeling, the 5 influencing factors of stream drying phenomena (soil erosion, forest growth, road-river disconnection, groundwater use, urban development) were selected and prepared as GIS-based time series spatial data from 1976 to 2015. The DrySAT-WFT was calibrated and validated from 2005 to 2015 at 8 multipurpose dam watershed (Chungju, Soyang, Andong, Imha, Hapcheon, Seomjin river, Juam, and Yongdam) and 4 gauging stations (Osucheon, Mihocheon, Maruek, and Chogang) respectively. The calibration results showed that the coefficient of determination (R²) was 0.76 in average (0.66 to 0.84) and the Nash-Sutcliffe model efficiency was 0.62 in average (0.52 to 0.72). Based on the 2010s (2006~2015) weather condition for the whole period, the streamflow impact was estimated by applying GIS data for each decade (1980s: 1976~1985, 1990s: 1986~1995, 2000s: 1996~2005, 2010s: 2006~2015). The results showed that the 2010s averaged-wet streamflow (Q95) showed decrease of 4.1~6.3%, the 2010s averaged-normal streamflow (Q185) showed decreased of 6.7~9.1% and the 2010s averaged-drought streamflow (Q355) showed decrease of 8.4~10.4% compared to 1980s streamflows respectively on the whole. During 1975~2015, the increase of groundwater use covered 40.5% contribution and the next was forest growth with 29.0% contribution among the 5 influencing factors.

• Journal of Food Hygiene and Safety
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• v.17 no.1
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• pp.36-44
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• 2002

GC-l00X is non-corrosive alkaline ionic water (pH 12). It is composed of hydroxyl radicals and supplemented with xylitol. Its antimicrobial activity was examined against 6 major food-borne pathogens; Staphylococcus aureus FRI 913, Salmonella enterica serova Enteritidis ATCC 13076, S. enterica serova Typhimurium Korean isolate, Vibrio parahaemolyticus ATCC 17803, Escherichia coli O157:H7 ATCC 43894 and Pseudomonas aeruginosa KCTC 1637 at three different temperatures (4$^{\circ}C$, $25^{\circ}C$ and 36$^{\circ}C$) with or without an organic material (2% yeast extract), respectively. The antimicrobial activities showed over 4 log-reductions (1.0$\times$10$^4$CFU/ml reduction) against all pathogens reacted at 37$^{\circ}C$ for 3 hours in the absence of the organic material. The activities showed same results when GC-l00X was diluted with same volume of distilled water or standard hard water (CaCO$_3$300 ppm). Its antimicrobial activity was more effective and quicker in Gram-negative bacteria than Gram-positive bacteria. Its washing efficacy against E. coli O157:H7 exposed to the surfaces of tomatoes (grapes) was compared with that of the other sanitizers such as other kitchen synthetic detergent and 100-ppm chlorine water. For the toxicological evaluation of the sanitizers, viable counts of E. coli O157:H7 penetrated into the core of tomatoes after washing products were also compared. The result revealed that GC-100X stock solution and its 5% diluted solution had similar washing effects to 100-ppm chlorine water and more effective than the other kitchen synthetic detergent. This result indicated that GC- l00X had antimicrobial activity and no toxicological side effects, therefore, could be useful for a new sanitizer to use in flood safety and kitchen hygiene.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.277-291
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• 2008

In order to carry out river basin management, it is necessary to evaluate the state of the river basin and make site-specific measures on the basis of management goals and objectives. A river basin is divided into several watersheds, which are composed of several components: water resources, social and economic systems, law and institution, user, land, ecosystems, etc. They are connected among them and form network holistically. In this study, a methodology for evaluating watershed management was developed by consideration of the various features of a watershed system. This methodology employed factor analysis to develop sub-indexes for evaluating water use management, environment and ecosystem management, and flood management in a watershed. To do this, first, the related data were gathered and classified into six groups that are the components of watershed systems. Second, in all sub-indexes, preliminary tests such as KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy and Bartlett's test of sphericity were conducted to check the data's acceptability to factor analysis, respectively. Third, variables related to each sub-index were grouped into three factors by consideration of statistic characteristics, respectively. These factors became indicators and were named, taking into account the relationship and the characteristics of included variables. In order to check the study results, the computed factor loadings of each variable were reviewed, and correlation analysis among factor scores was fulfilled. It was revealed that each factor score of factors in a sub-index was not correlated, and grouping variables by factor analysis was appropriate. And, it was thought that this indicator system would be applied effectively to evaluating the states of watershed management.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.