• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.5
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• pp.1-13
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• 2017

Recently, due to worldwide industralization and urbanization, natural environment has been severly damaged and global warning is worsening. Heat wave, torrential rainfall, typhoon and other natural disasters continuously occur due to global warming. Policies such as carbon emission regulation are taken into effect to solve such problems. Such global trend has affected interest to natural ecosystem and developed as a concept of ecosystem-services. This study particularly focused on ground water storage capacity among various ecosystem-services such as climate control and soil formation. It is because Korea suffers from drought and flood every year. Therefore, this study aims to understand objective numerical value of ecosystem-services value regarding ground water storage capacity of biotop classes based on relationship among precipitation, amount of evapotranspiration, and runoff of 7 regions of Chilgok-gun, Gyeongsangbuk-do and to convert the value into economic value. The study calculated ground water storage capacity based on relationship among precipitation, amount of evapotranspiration, and run off. Calculated amount of each capacity was 29.26 million ton($30.2mm/m^2$), 430.46 million ton($140.4mm/m^2$), 11.30 million ton($150.1.0mm/m^2$), 33 milion ton($3.0mm/m^2$). Economical value of ecosystem-service by each biotop classes appeared 4,128,800 thousand KRW ($21.32KRW/m^2$) for agricultural biotop, and 60,403,600 thousand KRW ($98.52KRW/m^2$) for forest biotop, 1,572,800 thousand KRW ($104.4KRW/m^2$) for grassland biotop, and 47,600 thousand KRW ($2.18KRW/m^2$) for bare ground biotop. The result of this study like above, it will be used as important evidentiary material to preserve natural resource effectively from various development business and policies that damages natural eco-system. Also, it is judged that ecosystem-service value by each land coverage will be used as important material for preparing legalistic institution such as establishing natural environment preservation plan, budget for alternative forest resource creation cost.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.511-529
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• 2022

The forest vegetation of the Korean Peninsula is dominated by deciduous Fagaceae forests. The study aimed to comparethe correlation between species composition and environmental factors in Namsan, Odaesan, and Ulleungdo. A vegetation survey of 75 sites was conducted from May to August 2018. Seven vegetation types were classified. The inland representative vegetation was classified as a Quercus mongolica community, and the island representative vegetation was classified as a Fagus multinervis community. The Quercus mongolica community was subdivided into the Aria alnifolia group, representative of cities, and the Tilia amurensis group, representative of mountainous regions. Analysis of important values and indicator species to examine the succession trends according to regional types showed that urban and island forestswere maintained as Fagaceae communities, and that mountainous region foreststransitioned to broadleaf species, such as Tilia amurensis and Carpinus cordata. A CCA analysis of vegetation type and site environmental factors showed that altitude had the biggest effect on species composition at the same latitude. The study results should contribute to a better understanding of the Korean Peninsula forest ecosystem characteristics and provide basic data for establishing a systematic conservation and restoration plan.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.67-84
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• 2023

In some areas of the Republic of Korea, the designation and management of conservation areas do not adequately reflect regional characteristics and often impose behavioral regulations without considering the local context. One prominent example is the Busan EDC area. As a result, conflicts may arise, including large-scale civil complaints, regarding the conservation and utilization of these areas. Therefore, for the efficient designation and management of protected areas, it is necessary to consider various ecosystem factors, changes in land use, and regional characteristics. In this study, we specifically focused on the Busan EDC area and applied machine learning techniques to analyze the habitat of regional species. Additionally, we employed Explainable Artificial Intelligence techniques to interpret the results of our analysis. To analyze the regional characteristics of the waterfront area in the Busan EDC district and the habitat of migratory birds, we used bird observations as dependent variables, distinguishing between presence and absence. The independent variables were constructed using land cover, elevation, slope, bridges, and river depth data. We utilized the XGBoost (eXtreme Gradient Boosting) model, known for its excellent performance in various fields, to predict the habitat probabilities of 11 bird species. Furthermore, we employed the SHapley Additive exPlanations technique, one of the representative methodologies of XAI, to analyze the relative importance and impact of the variables used in the model. The analysis results showed that in the EDC business district, as one moves closer to the river from the waterfront, the likelihood of bird habitat increases based on the overlapping habitat probabilities of the analyzed bird species. By synthesizing the major variables influencing the habitat of each species, key variables such as rivers, rice fields, fields, pastures, inland wetlands, tidal flats, orchards, cultivated lands, cliffs & rocks, elevation, lakes, and deciduous forests were identified as areas that can serve as habitats, shelters, resting places, and feeding grounds for birds. On the other hand, artificial structures such as bridges, railways, and other public facilities were found to have a negative impact on bird habitat. The development of a management plan for conservation areas based on the objective analysis presented in this study is expected to be extensively utilized in the future. It will provide diverse evidential materials for establishing effective conservation area management strategies.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.487-496
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• 2017

The marketization in North Korea is spreading rapidly. This study proposes forest rehabilitation strategy for North Korea in light of their major shift toward market economy. This current trend of marketization in North Korea is now affecting the forest sector, especially the way the residents utilize small forest land. For analyzing the influence of marketization on forest management in North Korea, we reviewed the official documents issued by North Korea and related materials of North Korean marketization. The government of Kim Jong Eun has set up policies and systems regarding the spread of marketization, such as guaranteeing individuals a right to dispose certain products on their own and establishing a special economic zone to attract foreign investments. In the forestry sector, the North Korean government has been trying to fully implement its forest restoration plan by carrying out measures like re-claiming of sloping lands that had been previously used by residents. However, as marketization progresses, it is expected that there lies much difficulty in government-led massive mobilization for forest restoration due to the increase of illegal logging to meet high demand for timber, illegal firewood harvesting, collecting non-timber products for livelihoods and illegal crop cultivation to sell in the market. Therefore, South Korea's support for forest restoration should also consider the recent marketization phenomenon in North Korea. It is necessary to formulate strategic measures such as conducting joint commercialization project on agroforestry management using cooperative farming unit, helping to improve income source from small forest lands, and to activate a comprehensive mountain village special economic zone by utilizing forest business. We do hope that our proposed forest rehabilitation strategy in this paper regarding the changes in North Korea's marketization and forest policy can give a meaningful suggestion on supporting forest restoration in North Korea in an effective way.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1001-1010
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• 2021

Smart factories are complex systems which combine latest information technology (IT) with operation technology (OT). A smart factory aims to provide manufacturing capacity improvement, customized production, and resource reduction with these complex technologies. Although the smart factory is able to increase the efficiency through the technologies, the security level of the whole factory is low due to the vulnerability transfer from IT. In addition, the response and restoration of the business continuity plan are insufficient in case of damage due to the absence of factory security experts. The cope with the such problems, we propose an information security practice content for analyzing the damage by generating ransomware attacks in a digital twin-based smart factory similar to the real world. In our information security content, we introduce our conversion technique of physical devices into virtual machines or simulation models to build a practical environment for the digital twin. This content generates two types of the ransomware attacks according to a defined scenario in the digital twin. When the two generated attacks are successfully completed, at least 8 and 5 of the 23 virtual elements are take damage, respectively. Thus, our proposed content directly identifies the damage caused by the generation of two types of ransomware in the virtual world' smart factory.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."