• Journal of the Korean Geotechnical Society
• /
• v.39 no.12
• /
• pp.23-35
• /
• 2023

The Ministry of Agriculture, Food and Rural Affairs has announced design standards for disaster-resilient greenhouses capable of resisting wind speeds with a 30-year frequency to respond to the destruction of greenhouses caused by strong winds. However, many greenhouses are still being maintained or newly installed as conventional standard facilities for the supply type. In these supply-type greenhouses, a small pile called a steel peg is used as reinforcement to resist wind-induced damage. The wind resistance of steel pegs varies depending on the soil environment and installation method. In this study, a correlation analysis was performed between the wind resistance of steel pegs installed in loam and sandy loam, using a soil hardness meter. To estimate the pull-out force of steel pegs based on soil water content and compaction, soil compaction tests and laboratory soil box and field tests were performed. The soil compaction degree was measured using a soil hardness meter that could easily confirm soil compaction. This was used to analyze the correlation between the soil compaction degree in the tests. In addition, a correlation analysis was performed between the pull-out force of steel pegs in the soil box and field. The findings of this study will be useful in predicting the pull-out force of steel pegs based on the method of steel peg installation and environmental changes.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.237-237
• /
• 2011

효율적인 지하수 관리를 위해서는 시공간적인 변동성을 고려한 지하수 함양률의 정량적 산정이 필수적이다. 본 연구에서는 지표수-지하수 연동해석이 가능하며 토지이용 특성과 국내 토양특성을 가장 잘 표현할 수 있는 한국형 장기 유출 모형 SWAT-K를 이용하여 진천지역의 분포형 지하수 함양량을 산정하였다. 행정경계와 수자원단위지도에서 제시하는 표준단위유역을 기준으로 하여 진천군을 포함하는 미호천유역을 SWAT-K 구동을 위한 모델영역으로 설정하여 주하도를 따라 34개의 소유역으로 구분하였다. SWAT-K를 구동하기 위해서는 기상 및 수문자료를 구축해야 하는데 강우량을 비롯하여 기온, 풍속, 일사량, 상대습도 등의 기상자료가 요구된다. 본 연구에서는 대상유역 내에 위치한 청주, 충주, 대전, 이천, 보은, 천안 기상관측소의 자료를 이용하여 기상자료를 구축하였으며, 모형의 계산시간, 모형결과의 정확도 등을 판단하여 30m 공간해상도를 가지는 DEM을 300m 공간 해상도로 가공하여 사용하였다. 토지이용도는 모의시 다양한 토지이용상태를 반영할 수 있도록 중분류(1:25,000) 토지이용도를 사용하였다. 토양도는 국립농업과학원에서 토양도 전산화 사업을 통해 구축된 1:25,000 축척의 정밀토양도를 사용하였다. SWAT-K를 이용하여 진천군을 포함한 미호천 전체유역에 대해 지표수-지하수 통합 물수지 분석 결과(2004년~2009년) 연평균 강수량 대비 유출률은 66.6%, 증발산률은 34.6%, 함양률은 20.8%로 나타났다. 지표수 유출과정과 지하수위 변동을 동시에 고려하여 산정한 소유역별 연간 함양량 결과를 산정하였고, 총 34개의 소유역별 연간 지하수 함양량을 제시하였다. 또한 SWAT-K 모형을 이용한 모델 영역중 진천군에 속하는 행정구역별, 표준권역별 연평균 함양량을 산출하였으며, 그 분석 결과 진천군 평균 함양률은 20.5%로 산정되었다.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.34 no.10
• /
• pp.901-906
• /
• 2010

A three-dimensional flow simulation was performed to investigate the flow field in a wind farm on a complex terrain. The present study aims to examine the effects of mountainous terrain and turbine arrangement on the performance of wind power generation. A total of 49 wind turbines was modeled in the computational domain; detailed blade shape of the turbines was considered. Frozen rotor method was used to simulate the rotating operation. The torque acting on the turbine blades was calculated to evaluate the performance of the wind turbines. The numerical results showed details of the flow structure in the wind farm including the velocity deficit in the separated flow regions; this velocity deficit was due to the topographical effect. The effect of the wake induced by the upstream turbine on the performance of the downstream wind turbine could also be observed from the results. The methodology of the present study can be used for selecting future wind-farm sites and wind-turbine locations in a selected site to ensure maximum power generation.

• Journal of the Korean association of regional geographers
• /
• v.12 no.3
• /
• pp.392-407
• /
• 2006

This paper was written on the purpose of examining and analyzing Sungho's view of geography in 'Cheonjimun(天地門)', a part of 'Sunghosaseol(星湖僿說)'. Sungho is not a geographer who specialized in geography, His view is neither structural in methodological approach nor profound in geographical thought. Unfortunately, he looks to be possessed by geomantic thought(風水地理思想) in explaining geographical features and native customs. And he focused and emphasized only on defensive function in place location. As a whole, however, he had a good grasp of and analyzed about geographical topics which are related to human life and we must take interest in. Therefore, in his view, there is a love for country and hometown. Especially, it has to be highly appreciated that he tried to explain his view in analytical and practical perspective with an unspoken advice which things necessary for human life have to be used to available knowledge.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.301-301
• /
• 2015

효율적인 지하수 관리를 위해서는 시공간적인 변동성을 고려한 지하수 함양량의 정량적 산정이 필수적이다. 본 연구에서는 지표수-지하수 연동해석이 가능하며 토지이용 특성과 국내 토양특성을 가장 잘 표현할 수 있는 한국형 장기 유출 모형 SWAT-K를 이용하여 장성지역의 분포형 지하수 함양량을 산정하였다. 행정경계와 수자원단위지도에서 제시하는 표준단위유역을 기준으로 하여 장성군을 포함하는 유역을 SWAT-K 구동을 위한 모델영역으로 설정하여 주하도를 따라 13개의 소유역으로 구분하였다. SWAT-K를 구동하기 위해서는 기상 및 수문자료를 구축해야 하는데 강우량을 비롯하여 기온, 풍속, 일사량, 상대습도 등의 기상자료가 요구된다. 본 연구에서는 대상유역 내에 위치한 광주, 정읍 기상관측소의 자료를 이용하여 기상자료를 구축하였으며, 모형의 계산시간, 모형결과의 정확도 등을 판단하여 30m 공간해상도를 가지는 DEM을 300m 공간해상도로 가공하여 사용하였다. 토지이용도는 모의시 다양한 토지이용상태를 반영할 수 있도록 중분류(1:25,000) 토지이용도를 사용하였다. 토양도는 국립농업과학원에서 토양도 전산화 사업을 통해 구축된 1:25,000 축척의 정밀토양도를 사용하였다. SWAT-K를 이용하여 장성군을 포함한 전체유역에 대해 지표수-지하수 통합 물수지 분석 결과(2005년~2013년) 연평균 강수량 대비 유출률은 63.0%, 증발산률은 34.6%, 함양률은 19.5%로 나타났다. 지표수 유출과정과 지하수위 변동을 동시에 고려하여 산정한 소유역별 연간 함양량 결과를 산정하였고, 총 13개의 소유역별 연간 지하수 함양량을 제시하였다. 또한 SWAT-K 모형을 이용한 모델 영역중 장성군에 속하는 행정구역별, 표준권역별 연평균 함양량을 산출하였으며, 그 분석 결과 장성군 평균 함양률은 20.3%로 산정되었다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.410-410
• /
• 2017

지하수 관리를 위해서는 시공간적인 변동성을 고려한 지하수 함양량의 정량적 산정이 필수적이다. 본 연구에서는 지표수-지하수 연동해석이 가능하며 토지이용 특성과 국내 토양특성을 가장 잘 표현할 수 있는 한국형 장기 유출 모형 SWAT-K를 이용하여 김천지역의 분포형 지하수 함양량을 산정하였다. 행정경계와 수자원단위지도에서 제시하는 표준단위유역을 기준으로 하여 김천시를 포함하는 유역을 SWAT-K 구동을 위한 모델영역으로 설정하여 주하도를 따라 19개의 소유역으로 구분하였다. SWAT-K를 구동하기 위해서는 기상 및 수문자료를 구축해야 하는데 강우량을 비롯하여 기온, 풍속, 일사량, 상대습도 등의 기상자료가 요구된다. 본 연구에서는 대상유역 내에 위치한 구미, 추풍령, 거창, 상주 기상관측소와 김천, 지례, 부항1, 부항2, 선산 강우관측소의 자료를 이용하여 기상 및 강우자료를 구축하였으며, 모형의 계산시간, 모형결과의 정확도 등을 판단하여 30m 공간해상도를 가지는 DEM을 300m 공간해상도로 가공하여 사용하였다. 토지이용도는 모의시 다양한 토지이용상태를 반영할 수 있도록 중분류(1:25,000) 토지이용도를 사용하였다. 토양도는 국립농업과학원에서 토양도 전산화 사업을 통해 구축된 1:25,000 축척의 정밀토양도를 사용하였다. SWAT-K를 이용하여 김천시를 포함한 전체유역에 대해 지표수-지하수 통합 물수지 분석 결과(2008년~2015년) 연평균 강수량 대비 유출률은 61.2%, 증발산률은 36.3%, 함양률은 18.0%로 나타났다. 지표수 유출과정과 지하수위 변동을 동시에 고려하여 산정한 소유역별 연간 함양량 결과를 산정하였고, 총 19개의 소유역별 연간 지하수 함양량을 제시하였다. 또한 SWAT-K 모형을 이용한 모델 영역중 김천시에 속하는 행정구역별, 표준권역별 연평균 함양량을 산출하였으며, 그 분석 결과 김천시 평균 함양률은 18.2%로 산정되었다.

• Journal of Convergence for Information Technology
• /
• v.11 no.3
• /
• pp.7-13
• /
• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.42 no.2
• /
• pp.127-136
• /
• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 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
• /
• v.41 no.1
• /
• pp.21-33
• /
• 2013

The purpose of this study is to find applicabilities of human bioclimatic maps, using human thermal sensation(comfort) in summer, with microclimatic in situ data and computer simulation results at the study site of downtown Daegu. This includes the central business district(CBD) area and two urban parks, the Debt Redemption Movement Memorial Park and the 2.28 Park, for urban and landscape planning and design. Climatic data and urban setting information for the analysis of human thermal sensation were obtained from in situ measurement and the geographic information system data. As a result, the CBD had higher air temperature than the parks when the wind speed was low. Relative humidities were opposite to the air temperature. Especially, same directional streets with local wind direction had lower air temperature than streets perpendicular to the wind direction. The most important climatic variable of human thermal sensation in summer was direct beam solar radiation. Also, creating shadow areas would be the most relevant method for modifying hot thermal environments in urban areas. The most effective method of creating shadow patterns was making a tree shadow over a pergola, and the second best one was making a tree shadow on the front of north directional building walls. Moreover, how to plant trees for creating shadow patterns was important as well as what kind of trees should be planted. The results of human thermal sensation were warm to very hot at sunny areas and neutral to warm at shaded ones. At the sunny areas, wide, squared shape areas had a little bit higher thermal sensation than those of narrow streets. The albedo change of building walls 0.15 and ground surface 0.1 could change 1/6 of a sensation level at the shaded areas and 1/3 at the sunny ones. These microclimatic approaches will be useful to find appropriate methods for modifying thermal environments in urban areas.