• Korean Journal of Soil Science and Fertilizer
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• v.32 no.1
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• pp.47-56
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• 1999

$^{15}N$-Isotope concentrations of groundwater from l4 wells with different land-use types in Kyonggi Province were measured to investigate the nitrate contamination sources. Water samples were collected monthly from January to December 1997 and analyzed for pH. PC, anions (fluoride, chloride, nitrate, sulfate, inorganic phosphate, and bicarbonate), and canons (calcium, magnesium, potassium, and sodium). For the analysis of the $^{15}N/^{14}N$ ratio as ${\delta}^{15}N$, $N_2$ samples were prepared through Kjeldahl-Rittenberg method and were analyzed using an isotope ratio mass spectrometer (VG Optima IRMS). Reproducibility of the method and precision of the IRMS were below 1.0‰ and 0.1‰, respectively. The ionic composition of each groundwater sample was only slightly different according to the land-use type. The nitrate concentrations of groundwater in cropland or livestock farming areas were higher than those in the residential area. The percentages of nitrate to total anions of groundwater samples from the livestock farming area were higher than those of other areas. The ${\delta}^{15}N$ values of ammonium sulfate, urea, groundwater sample in the non-contaminated area, and water from the animal manure septic tank were -2.7, 1.4, 5.5, and 27.2‰, respectively. Based on the ${\delta}^{15}N$ values, the sources of nitrate could be classified as originated from chemical fertilizers with ${\delta}^{15}N$ values below 5% and as from animal manure or municipal waste with ${\delta}^{15}N$ values over 10‰. In most cases, contamination sources investigated from ${\delta}^{15}N$ values of groundwater samples were correlated with the specific sources according to the land-use types. However, some ${\delta}^{15}N$ values did not matched the apparent land-use types, and there were seasonal variations of ${\delta}^{15}N$ values within the same well. These results suggest that the groundwater quality was affected by two or more contamination sources and the contribution of each source to the groundwater quality varied depending on the sampling season.

• Journal of Distribution Science
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• v.9 no.4
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• pp.43-50
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• 2011

Concerning the risk factors of the outbreak of a fire in a traditional market, most of those markets are located in downtown areas or residential areas; thus, although their location may be favorable in terms of marketability, they face a potential risk in that a fire may develop into a large blaze owing to poor environment or the absence of facilities prepared for disaster during a fire. Moreover, as many people are densely poised in the markets, it is very probable that a fire may occur owing to the excessive use of heaters in the winter as well as the reckless use of electric and gas facilities. It seems that traditional markets encounter difficulty being insured against fire, because of their vulnerability and that the vast majority of small-scale sellers are likely to suffer mental anguish and tremendous physical injury in case of a fire. However, most of those sellers in the traditional markets are hand-to-mouth sellers, and they lack awareness of safety concerns and have insufficient experience in safe facility management. As small-scale sellers constitute the majority in the traditional market, the subscription rate of fire insurance in most of the traditional markets is low for the reasons of their needy circumstances and their financial burden. Statistically, the subscription by street vendors is non-existent; therefore, these vendors have a fairly limited access to indemnification after fire damage. Because of these problems, this study's purpose is to identify the current level of insurance subscription by these markets, which are exposed to poor facilities and vulnerability to fire. In order to fix this, it appears that shop owners and consumers will have to band together. For this study, we executed a fire policyholder fact-finding mission at traditional markets with approximately 108 and 981 stores. The research method was executed by an investigation using one-on-one individual interviews using a questionnaire. The contents investigated current insurance subscriptions. The method of analysis looked at the difference of insured amount according to volume size through cross-tabulation of the difference of insured amount by possession form, difference of insured amount by market form, difference of insured amount by category of business, difference of insured amount by market size, etc. Furthermore, the study should be used to propose solutions for problems through theoretical review with the use of a literature research, because the field case study was through interviews with the persons concerned, and the survey of the current insurance subscriptions by traditional market shopkeepers. The traditional market would generally have difficulty affording fire insurance. Fire insurance subscription rates of most of the market proved to be inactive, because of the economic burden of payment. Lack of funds is thought to be the main factor that causes a lack of realization about the necessity of fire insurance. In addition to expensive insurance premiums, sometimes, the companies' valuation of the businesses is lower than their actual valuations, and they do not pay out enough during a claim. The research presents an improvement plan that, when presented at the traditional markets, may strengthen their ability to procure fire insurance through the help of the central government. Researchers connected with the traditional market mainly accomplish the initial research. However, although this research has its limitations, it offers considerable benefits. For future researchers, I would suggest looking at several regions for comparison.

• Journal of Radiation Protection and Research
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• v.40 no.3
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• pp.124-131
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• 2015

Radiation exposure to humans can be caused by the gamma rays emitted from natural radioactive elements(such as uranium, thorium and potassium and any of their decay products) of Naturally Occurring Radioactive Materials(NORM) or Technologically Enhanced Naturally Occurring Radioactive Materials(TENORM) added consumer products. In this study, assume that activity of radioactive elements is $^{238}U$, $^{235}U$, $^{232}Th$ $1Bq{\cdot}g^{-1}$, $^{40}K$ $10Bq{\cdot}g^{-1}$ and the gamma rays emitted from these natural radioactive elements radioactive equilibrium state. In this study, reflected End-User circumstances and evaluated annual exposure dose for products based on ICRP reference voxel phantoms and ICRP Recommendation 103 using the Monte Carlo Method. The consumer products classified according to the adhere to the skin(bracelet, necklace, belt-wrist, belt-ankle, belt-knee, moxa stone) or not(gypsum board, anion wallpaper, anion paint), and Geometric Modeling was reflected in Republic of Korea "Residential Living Trend-distributions and Design Guidelines For Common Types of Household.", was designed the Room model($3m{\times}4m{\times}2.8m$, a closed room, conservatively) and the ICRP reference phantom's 3D segmentation and modeling. The end-user's usage time assume that "Development and Application of Korean Exposure Factors." or conservatively 24 hours; in case of unknown. In this study, the results of the effective dose were 0.00003 ~ 0.47636 mSv per year and were confirmed the meaning of necessary for geometric modeling to ICRP reference phantoms through the equivalent dose rate of belt products.

• Journal of the Korean association of regional geographers
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• v.3 no.1
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• pp.63-80
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• 1997

This study attempts to establish the criteria of site selection for establishing solid waste disposal facility, to determine optimal solid waste disposal sites with the criteria, and to examine the suitability of the selected sites. The Multi-Criteria Evaluation(MCE) module in Idrisi is used to determine optimal sites for solid waste disposal. The MCE combines the information from several criteria in interval and/or ratio scale to form a single index of evaluation without leveling down the data scale into ordinal scale. The summary of this study is as follows: First, the considerable criteria are selected through reviewing the literature and the availability of data: namely, percent of slope, fault lines, bedrock characteristics, major residential areas, reservoirs of water supply, rivers, inundated area, roads, and tourist resorts. Second, the criteria maps of nine factors have been developed. Each factor map is standardized and multiplies by its weight, and then the results are summed. After all of the factors have been incorporated, the resulting suitability map is multiplied by each of the constraint in turn to "zero out" unsuitable area. The unsuitable areas are discovered in urban district and its adjacencies, and mountain region as well as river, roads, resort area and their adjacency districts. Third, the potential sites for establishing waste disposal facilities are twenty five districts in Youngyang-gun. Five districts are located in Subi-myun Sinam-ri, nine districts in Chunggi-myun Haehwa-ri and Moojin-ri, and eleven districts in Sukbo-myun Posan-ri. The first highest score of suitability for waste disposal sites is shown at number eleven district in Chunggi-myun Moojin-ri and the second highest one is discovered at number twenty one district in Sukbo-myun Posan-ri that is followed by number nine district in Chunggi-myun Haehwa-ri, number seventeen and twenty three in Sukbo-myun Posan-ri, and number two in Subi-myun Sinam-ri. The first lowest score is found in number six district in Chunggi-myun Haehwa-ri, and the second lowest one is number five district in Subi-myun Sinam-ri. Finally, the Geographic Information System (GIS) helps to select optimal sites with more objectively and to minimize conflict in the determination of waste disposal sites. It is important to present several potential sites with objective criteria for establishing waste disposal facilities and to discover characteristics of each potential site as a result of that final sites of waste disposal are determined through considering thought of residents. This study has a limitation of criteria as a result of the restriction of availability of data such as underground water, soil texture and mineralogy, and thought of residents. To improve selection of optimal sites for a waste disposal facility, more wide rage of spatial and non-spatial data base should be constructed.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.6
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• pp.1-11
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• 2011

The research was aimed to verify the feasibility of the model of Park-Based Mixed-Use Districts(PBMUD) around urban large park to secure private-based urban parks through the revision of the urban zoning system. The PBMUD is a type of urban zoning district in which park-oriented land use is mixed with the urban land uses of residents, advertising, business, culture, education and research. The PBMUD, delineated from and based on a new paradigm of landscape urbanism, is a new urban strategy to secure urban parks and to cultivate urban regeneration around parks and green spaces to enhance the quality of the urban landscape and to ameliorate urban environmental disasters like climate change. This study performed a questionnaire survey and analysis after a review of literature related to PBMUD. The study looked for specialists in the fields of urban planning and landscape architecture such as officials, researchers and engineers to respond to the questionnaire, which asked about degree of preference. The conclusions of this study were as follows. Firstly, specialists prefer the PBMUD at 79.3% for to 20.7% against ratio, indicating the feasibility of the model of PBMUD. The second, the most preferable reasons for the model, were the possibility of securing park space around urban parks and green spaces that assures access to park and communication with each area. The third, the main reason for non-preference for the model, was a lack of understanding of PBMUD added to the problems of unprofitable laws and regulations related to urban planning and development. These proposed a revision of the related laws and regulations such as the laws for planning and use of national land, laws for architecture etc. The fourth, the most preferred type of PBMUD, was cultural use mixed with park use in every kind of mix of land use. The degree of preference was lower in the order of use of commercial, residential, business, and education(research) when mixed with park use. The number of mixed-use amenities with in the park was found to be an indicator determining preference. The greater the number, the lower was preference frequencies, especially when related to research and business use. The fifth, the preference frequencies of the more than 70% among the respondents to the mixed-use ratio between park use and the others, was in a ratio of 60% park use and 40% other urban use. These research results will help to launch new future research subjects on the revision of zoning regulations in the laws for the planning and uses of national land and architectural law as well as criteria and indicators of subdivision planning as related to a PBMUD model.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.