• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.295-303
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• 2012

Clarify wind energy productivity depends on three factors: the wind probability density function(PDF), the turbine's power curve, and the air density. The wind PDF gives the probability that a variable will take on the wind speed value. Wind shear refers to the change in wind speed with height above ground. The wind speed tends to increase with the height above ground. also, Wind PDF refers to the change with height above ground. Wind analysts typically use the Weibull distribution to characterize the breadth of the distribution of wind speeds. The Weibull distribution has the two-parameter: the scale factor c and the shape factor k. We can use a linear least squares algorithm(or Ln-least method) and moment method to fit a Weibull distribution to measured wind speed data which data was located same site and different height. In this study, find that the scale factor is related to the average wind speed than the shape factor. and also different types of terrain are characterized by different the scale factor slop with height above ground. The gross turbine power output (before accounting for losses) was caculated the power curve whose corresponding air density is closest to the air density. and air desity was choose two way. one is the pressure of the International Standard Atmosphere up to an elevation, the other is the measured air pressure and temperature to calculate the air density. and then each power output was compared.

• Journal of Korean Society of Forest Science
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• v.90 no.6
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• pp.781-787
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• 2001

The spread rate of forest fire was analyzed on Samcheok forest fire that broke out on April 7, 2000 in Kunduck-Myun, Samcheok-City, Kangwon-Province and lasted for about 9 days. The spatial database including topography, overstory species distribution, micro-climate, daily fire front lines for the area was built using GIS and the daily spread pattern was investigated to determine a multiple regression equation to estimate forest fire spread rate. The results of the investigation showed that, on the first day, the forest fire spreaded out extremely fast up to 12.3m/min at about 10 a.m. until noon. After that, the forest fire spread rate fluctuated and slowed down as low as below 1m/min and quenched on April 15. The daily area-based spread rate along the fire spread line got to the peak of about 5,700ha on April 11, of which spread rates were recorded as 2.84m/min in the first half and 1.10m/min in the second half. Also, it was found that slope aspect, wind velocity and % area distribution of Pinus densiflora are the major factors affecting the spread rate of forest fire in this area.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.209-210
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• 2016

우리나라의 연평균강수량은 약 1362 mm이며, 총강수량의 약 30% 이상이 증발산을 통해 손실되고 있다고 추정되어지고 있다. 증발산은 물 수지 분석에 있어 매우 중요한 성분이며, 많은 부분을 차지하지만 다른 요인들에 비해 직접적인 관측이 어려워 과거에는 경험식을 사용하거나 단순하게 가정에 의해 결정해 왔다. 또한 기상자료로부터 증발산량을 추정하거나 증발접시나 추정식으로 잠재증발산을 추정하고 있다. 또한 최근 기후변화의 가속화에 따른 홍수의 가뭄의 강도와 빈도가 높아지고 있으며, 이에 따라 수자원 관리에 있어서 기초수문조사 항목에 많은 변화를 요구하고 있다. 그 결과 2007년 4월 하천법 개정으로 증발산량 및 토양수분량이 기초수문조사 항목으로 추가되었으며, K-water 연구원에서는 용담시험유역에 플럭스타워를 설치하였고 현재 운영 중에 있다. 덕유산 플럭스타워는 용담시험유역 내에 위치한 금강 수계 구량천 상류부의 덕곡제 유역 내에 설치하였으며, 2011년 4월부터 실제 증발산량을 관측하고 있다. 동경 $127^{\circ}$42'23" ~ $127^{\circ}$44'53", 북위 $35^{\circ}$50'47" ~ $35^{\circ}$52'50"사이로 중부지방에 위치한 유일한 증발산관측 타워이다. 유역 면적은 9.27 km2으로 유로연장 3.48 km, 유역 평균폭 2.66 km, 형상계수는 0.77이며, 덕곡제플럭스 타워 주변의 토지이용은 대부분 산림으로 구성되어 있으며, 침활 혼효림과 낙엽송림으로 임상 분포가 이루어져 있다. 주요 관측기기로는 3차원 풍향 풍속계, $CO_2/H_2O$ 기체분석기, 순복사 측정 센서, 지중열플럭스 측정 센서 등이 있다. 2011년부터 측정된 자료를 바탕으로 에디공분산 방법을 이용하여 증발산량을 측정하였으며, 30분간의 데이터 18,000개 중 취득률 90 % 이상의 데이터를 대상을 분석을 실시하였다. 2011 ~ 2015년도 증발산량 분석 결과는 아래의 표와 같다. 증발산의 패턴은 1월부터 서서히 증가하지만 활발하지는 않고, 4월부터 매우 활발해져 8월에 최대치에 이른다. 10월부터 증발산량은 급격히 감소하기 시작하며 11, 12월에는 증발산이 거의 발생하지 않는 공통적인 경향을 보였다. 2013년 8, 9월은 다른 해와 다른 경향을 보이고 있는데, 이는 2013년 8, 9월에 강우가 많이 발생하여 증발산량이 감소하였기 때문으로 판단된다. 2015년 8월은 다른 년도와 비교했을 때, 매우 높은 증발산량을 보이는데 이는 2015년 8월에 많은 강우에도 식생이 활발하게 작용하였기 때문으로 판단된다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.251-258
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• 2022

Donghae Port is adjacently located to a residential area wherein 26,933 generations are creating a living environment. The areas comprise Song-jeong village (5,754 generations) and Bukp-yeong village (21,179 generations). Major cargoes handled in Donghae Port are dusty limestone, cement, anthracite, and bituminous coal, etc. In the process of handling such cargoes, air pollutants including oxide dust and fine dust which adversely impact the living conditions and health of residents are generated, causing air pollution in the vicinity of the port. Currently, Donghae Port is making an effort to improve the operation environment of the infrastructure and equipment in stages, for the purpose of reducing air pollutant emissions caused by the port industries in a long-term perspective. In this study, the sphere of influence of fine dust exposure and the degree of air pollution in the surrounding area were analyzed such as the state of fine dust concentration and diffusion in the vicinity of Donghae Port, fine dust diffusion pattern and spatial distribution of high-concentration considering wind direction and speed characteristics during the day and seasonal cycles. A more effective plan to reduce the concentration of fine dust in nearby areas by combining reduction plan, is being developed in terms of improvement regarding port infrastructure and equipment, and reduction measures considering the characteristics of the atmosphere environment according to the daytime, nighttime and season.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.100-119
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• 2020

During recent years, the heat environment and particulate matter (PM₁₀) have become serious environmental problems, as increases in heat waves due to rising global temperature interact with weakening atmospheric wind speeds. There exist urban heat islands and urban pollution islands with higher temperatures and air pollution concentrations than other areas. However, few studies have examined these issues together because of a lack of micro-scale data, which can be constructed from spatial data. Today, with the help of satellite images and big data collected by private telecommunication companies, detailed spatial distribution analyses are possible. Therefore, this study aimed to examine the spatial distribution patterns of urban heat islands and urban pollution islands within Busan Metropolitan City and to compare the distributions of the two phenomena. In this study, the land surface temperature of Landsat 8 satellite images, air temperature and particulate matter concentration data derived from a private automated meteorological observation system were gridded in 30m × 30m units, and spatial analysis was performed. Analysis showed that simultaneous zones of urban heat islands and urban pollution islands included some vulnerable residential areas and industrial areas. The political migration areas such as Seo-dong and Bansong-dong, representative vulnerable residential areas in Busan, were included in the co-occurring areas. The areas have a high density of buildings and poor ventilation, most of whose residents are vulnerable to heat waves and air pollution; thus, these areas must be considered first when establishing related policies. In the industrial areas included in the co-occurring areas, concrete or asphalt concrete-based impervious surfaces accounted for an absolute majority, and not only was the proportion of vegetation insufficient, there was also considerable vehicular traffic. A hot-spot analysis examining the reliability of the analysis confirmed that more than 99.96% of the regions corresponded to hot-spot areas at a 99% confidence level.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.1
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• pp.39-50
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• 2014

This paper reviews the results of recent observations in the Yeonsuri valley of Mt. Youngmun during springtime (March to May) in 2012. Automated weather stations were installed at twelve sites in the valley to measure temperature and 2, 3 dimensional wind. We examined temporal and spatial characteristics of temperatures and wind data. The Yeonsuri valley springtime average temperature lapse rate between the top and bottom of the entire period is $-0.44^{\circ}C/100$ m. It can be changed by the synoptic weather conditions, the lapse rates is greatest in order of clear days ($-0.48^{\circ}C/100$ m), rainy ($-0.41^{\circ}C/100$ m) and cloudy days ($-0.40^{\circ}C/100$ m). In the night, the temperature inversion layer (thermal belt) and the cold pool are formed within the valley. In addition, we measured temperature and wind distribution from the bottom to 3.5 m, the cold layers existed up to 1.5 m, which were affected by ground mixed layer. The results will provide useful guidance on agricultural practices as well as model simulations.

• 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.50 no.2
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• pp.1-22
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• 2022

The modification effects of street trees on outdoor thermal comfort in summertime according to tree planting types and road direction were analyzed using a computer simulation program, ENVI-met. With trees, the air temperature and wind speed decreased, and the relative humidity increased. In the case of mean radiant temperature (T_mrt) and human thermal sensation, physiological equivalent temperature (PET) and universal thermal climate index (UTCI), there was a decrease during the daytime. The greatest change among the meteorological factors by trees happened in Tmrt, and PET and UTCI showed similar patterns with T_mrt·The most effective tree planting type on thermal comfort modification was low tree height, wide tree crown, high leaf area index, and narrow planting interval (LWDN). T_mrt, PET and UTCI showed a large difference depending on shadow patterns of buildings and trees according to solar altitude and azimuth angles, and building locations. When the building shade areas increased, the thermal modification effect by trees decreased. In particular, results on the east and west sidewalks showed a large deviation over time. When applying the LWDN, the northwest, west and southwest sidewalks showed a significant reduction of 8.6-12.3℃ PET and 4.2-4.5℃ UTCI at 10:00, and the northeast, east and southeast sidewalks showed 8.1-11.8℃ PET and 4.4-5.0℃ UTCI at 16:00. On the other hand, when the least effective type (high tree height, narrow tree crown, low leaf area index, and wide planting interval) was applied, the maximum reduction was up to 1.8℃ PET and 0.9℃ UTCI on the eastern sidewalks, and up to 3.0℃ PET and 0.9℃ UTCI on the western ones. In addition, the difference in modification effects on T_mrt, PET and UTCI between the tree planting types was not significant when the tree effects were reduced by the effects of buildings. These results can be used as basic data to make the most appropriate street tree planting model for thermal comfort improvement in urban areas in summer.