• Journal of Environmental Science International
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• v.31 no.9
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• pp.803-813
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• 2022

In this study, we improved the water-based condensation particle counter in Atmospheric Research Aircraft NARA and investigated the condensation particle number concentration over the Korean peninsula. Pump and set point information were changed to improve the instrument used by aircraft for observation. Ground-based observational result showed that the error between two instruments, which are water-based condensation particle counter and butanol-based condensation particle counter, was 4.7%. Aerial observational result revealed that the number concentration before improvement indicate large variation with unstable condition, whereas the number concentration after improvement indicate a reasonable variation. After improvement, the number concentration was 706±499 particle/cm³ in the West Sea and 257±80 particle/cm³ in Gangwon-do, and these are similar to the concentration range reported in previous studies. Notably, this is the first attempt to use aerial observation with water-based condensation particle counter to investigate condensation particle number concentration.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.248-248
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• 2023

In recent years, the number of landslides in Korea has been increasing due to extreme weather events such as localized heavy rainfall and typhoons. Landslides often occur with debris flows, land subsidence, and earthquakes. They cause significant damage to life and property. 64% of Korea's land area is made up of mountains, the government wanted to predict landslides to reduce damage. In response, the Korea Forest Service has established a 'Landslide Information System' to predict the likelihood of landslides. This system selects a total of 13 landslide factors based on past landslide events. Using the LR technique (Logistic Regression) to predict the possibility of a landslide occurrence and the accuracy is known to be 0.75. However, most of the data used for learning in the current system is on landslides that occurred from 2005 to 2011, and it does not reflect recent typhoons or heavy rain. Therefore, in this study, we will apply a total of six machine learning techniques (KNN, LR, SVM, XGB, RF, GNB) to predict the occurrence of landslides based on the data of Inje, Gangwon-do, which was recently produced by the National Institute of Forest. To predict the occurrence of landslides, it is necessary to process converting landslide events and factors data into a suitable form for machine learning techniques through ArcGIS and Python. In addition, there is a large difference in the number of data between areas where landslides occurred or not. Therefore, the prediction was performed after correcting the unbalanced data using Tomek Links and Near Miss techniques. Moreover, to control unbalanced data, a model that reflects soil properties will use to remove absolute safe areas.

• Journal of Forest and Environmental Science
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• v.39 no.3
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• pp.128-139
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• 2023

Urban forests serve multiple purposes by providing green resting spaces and environmental benefits for city residents. In the old city center, where parks are scarce, the campus of Kangwon National University, Chuncheon, Gangwon-do, South Korea, serves as an urban forest for students, faculty, and citizens. This study aims to quantitatively analyze the environmental functions of green spaces on campus, raising awareness about their importance among campus members. The total carbon storage of campus trees was estimated at 1,653,218 kg, including 1,512,586 kg in forest areas, 131,061 kg in planting spaces around buildings, and 9,571 kg in street spaces. The annual carbon uptake of campus trees was estimated to be 39,391 kg/year, with 30,144 kg/year in forest areas, 8,017 kg/year in planting spaces around buildings, and 1,230 kg/year in horizontal spaces. In addition, annual oxygen production was estimated to be 105,044 kg/year, with 80,385 kg/year in forest areas, 21,378 kg/year in planting spaces around buildings, and 3,281 kg/year in street spaces. Furthermore, we estimated carbon emissions from the use of on-campus facilities to be 4,856,182 kg/year, while oxygen consumption by members was estimated at 53,975 kg/year. However, the campus trees supplied a sufficient amount of oxygen, which was twice the amount required by school members. The carbon uptake amount was approximately 1% of the amount of carbon emissions, resulting in a modest contribution to improving the environmental conditions of the site.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.4
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• pp.568-582
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• 2022

This study identifies regional problems caused by the relocation and dismantlement of military units by the government's Defense Reform 2.0 and examines the current situation. This study provides policy implications for future regional development by discussing the use of military idle land in the border region and seeking how to revitalize the region through urban regeneration. In order to utilize and manage the military idle land, first, the mutually beneficial development of the military unit and the local community must be achieved through the establishment of governance between private, government, and military sectors. Second, review and improvement of laws and systems related to border regions and military idle lands should be accompanied. Third, the various ideas and policies that reflect the uniqueness of the border region must be established. Finally, this study provided implications for the efficient utilization and management of sustainable idle land by reflecting the specificity of the border region in the future.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Korean Journal of Social Welfare Studies
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• v.44 no.2
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• pp.89-112
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• 2013

This study was aimed to investigate the status and the relation of problem drinking, the influence of parents' problem drinking and codependency among students in Dept. of Social Welfare for students to become competent social workers on problem drinking, which is a serious social problem in our society. The subjects of this study consisted of 303 persons who were the university students of Dept. of Social Welfare in the east of Gangwon-do. The data were collected through self-reported questionnaires from Nov. 22th to Dec. 12th, 2012. Results indicated that 91.1% of students were drinkers, the average of problem drinking based on international standard of AUDIT was 8.33, and problem drinking showed significantly in sex. 30% of students were influenced by parents' problem drinking, both the influence of parents' problem drinking and codependency showed significantly in family income. The average codependency of students was mild level and the influence of parents' problem drinking contributed significantly to the codependency. Implications of findings of this study were discussed.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2022.09a
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• pp.42-42
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• 2022

본 연구는 한반도의 중심 생태 축인 백두대간의 최북단이자 산림유전자원보호구역인 향로봉(1296m) 및 칠절봉(1172m), 둥굴봉(1305m)의 관속식물상을 조사하여 생태적 가치를 연구하고 생물 종 다양성 보전을 위해 진행하였다. 향로봉을 중심으로 2021년 6월부터 2022년 8월까지 총 5회 현장 조사를 실시한 결과, 총 70과 181속 237종 12아종 21변종 1품종 등 총 271분류군으로 확인되었다. 산림청 지정 희귀식물은 총 7분류군으로, 멸종위기(CR) 등급은 날개하늘나리, 끈끈이장구채, 봉래꼬리풀로 3분류군, 취약(VU) 등급은 만삼, 금강초롱꽃 등 2분류군, 위기(EN) 등급은 두메닥나무, 국화방망이로 2분류군이 확인되었다. 북방계식물은 껍질용수염, 개시호, 만삼 등 80분류군으로, 전체 분류군 중 28.8%를 차지하는 것으로 확인되었다. 한국특산식물은 한라사초, 할미밀망, 토현삼 등 17분류군이 확인되었다. 외래식물은 서양민들레, 애기수영, 토끼풀 등 11분류군이 확인되어 전체 분류군 중 3.9%를 차지하는 것으로 나타났다. 확인된 식물구계학적 특정식물 중 V급은 날개하늘나리, 솜다리 등 2분류군으로 나타났으며, 그 외 IV급 15분류군, III급 25분류군, II급 38분류군, I급 14분류군이 확인되었다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.30 no.1
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• pp.23-30
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• 2024

In this study, we investigated the use of Gondre, a special product from Gangwon-do, as an ingredient in Kimchi. Kimchi added with Gondre was manufactured for further analysis. The antioxidative properties of Kimchi with uncooked Gondre were found to be 1.2 times higher than those of Kimchi with boiled Gondre, suggesting that uncooked Gondre is the preferred additive. To assess the effect of Gondre over a 30-day period at 5℃, Kimchi was prepared with Gondre at mixed ratios of 20%, 40%, and 50% (w/w). No significant effects of Gondre on pH, titrated acidity, or microorganism growth were observed. However, sensory evaluation results indicated that Kimchi with 20% (w/w) Gondre was preferred over other ratios.