• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.420-428
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• 2019

Although marine structures are a risk factor interfering with the passage of ships, there are no obvious guidelines on the required safety distance between ships and marine structures under regulations and laws. In this study, the width of the shipping route width was set based on the AIS data to analyze the separation distance between marine structures and ships, and the ships were classified by the length of each ship. By analyzing the distribution at marine structures, this study confirmed that the ships' traffic volume was in the form of normal distribution. To statistically analyze the separation distance between the traffic distribution results and the normal distribution of ships in this study, the traffic pattern analysis around the marine structures was performed. As a result, the traffic pattern was different by length and the recommended safety distance for each length is presented accordingly. Referring to the IMO (International Maritime Organization) the standard turning circle and reference of safety separation distance between ships and offshore wind turbines of the CESMA (Confederation of European Shipmasters' Associations) and P IANC (World Association for Waterborne Transport Infrastructures), the analysis was conducted on ships that did not follow the set distance among the AIS data by setting the distance within the recommended ship safety distance to 5-7 overall length. As a result, the 5.5 length over all of the safety recommendations were selected as appropriate, and based on the above results, the two cases recommending ship safety distance were proposed.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.

• Journal of Biosystems Engineering
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• v.12 no.2
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• pp.16-27
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• 1987

Greenhouse farming was introduced to the Korean farmers in the middle of 1950's and its area has been increased annually. The plastic greenhouse, which is covered with polyethylene or polyvinyl chloride film, has been rapidly spread in greenhouse farming since 1970. The greenhouse farming greatly contributed to the increase of farm household income and the improvement of crop productivity per unit area. Since the greenhouse farming is generally practiced during winter, from November to March, the thermal environment in the plastic greenhouse should be controlled in order to maintain favorable condition for plant growing. Main factors that influence the thermal environment in the plastic greenhouse are solar radiation, convective and radiative heat transfer among the thermal component of the greenhouse, and the use of heat source. The objective of this study was to develop a simulation model for thermal environment of the plastic greenhouse in order to determine the characteristics of heat flow and effects of various ambient environmental conditions upon thermal environments within the plastic greenhouse. The results obtained are summarized as follows: 1. Simulation model for thermal environment of the plastic greenhouse was developed, resulting in a good agreement between the experimental and predicted data. 2. Solar radiation being absorbed in the plant and soil during the daytime was 75 percent of the total solar radiation and the remainder was absorbed in the plastic cover. 3. About 83 percent of the total heat loss was due to convective and radiative heat transfer through the plastic cover. Air ventilation heat loss was 5 to 6 percent of total heat loss during the daytime and 16 to 17 percent during the night. 4. The effectiveness of thermal curtain for the plastic greenhouse at night was significantly increased by the increase of the inside air temperature of the greenhouse due to the supplementary heat. 5. When the temperature difference between the inside and outside of the greenhouse was small, the variation of ambient wind velocity did not greatly affect on the inside air temperature. 6. The more solar radiation in the plastic greenhouse was, the higher the inside air temperature. Because of low heat storage capacity of the plant and soil inside the greenhouse and a relatively high convective heat loss through the plastic cover, the increase of solar radiation during the daytime could not reduce the supplymentary heat requirement for the greenhouse during the night.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.4
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• pp.315-326
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• 2015

Temporal and spatial variations of surface water temperature in Jinju Bay for the period of 2010~2011 were studied using the data from temperature monitoring buoys deployed at 17 stations in the south coast of Korea. Water temperature shows the maximum late in January and the minimum early in August. Seasonal variation of water temperatures at the north part of the bay is smaller than the middle and the south. In summer, the lowest and the highest of maximum water temperature are distributed around Jijok Channel which is located at the south of the bay. The fluctuations of water temperatures at Noryang and Daebang Channel are smaller than others because of vertical mixing caused by passage of strong tidal currents. Wind and strong currents affect on the stratification of the surface water layer near Daebang Channel. High temperatures come in frequently around the north area when eastward constant flows appear at neap tide as blowing westerly in the springtime at Noryang Channel. Spectral analyses of temperature records show significant peaks at 7~20 day periods at Noryang Channel, 7~20 day and semidiurnal at the west coast of Changsun Island and Jijok Channel and 7~20 day and diurnal at the middle of the bay. Temperature fluctuation at Noryang Channel shows high coherence and has leading phase with those at other stations in the bay. However, the phase of temperature fluctuation at Noryang Channel falls behind that at Daebang Channel. Daebang Channel has an influence on the temperature fluctuation only at the west and middle part of the bay. Cross-correlation analyses for the temperature fluctuation show that Jinju Bay could be classified into six areas; Noryang Channel, the area of convergence and divergence at the north, Daebang Channel, the west coast of Changsun Island, the mixing area at the middle of the bay and the south inside of the bay, respectively.

• Journal of the Korean Society for Railway
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• v.15 no.6
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• pp.579-587
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• 2012

Shin-gumho station in Seoul underground subway have been selected to be experimentally investigated and analyzed for the real air supply & exhaust capacity compared to the original capacity of ordinary and emergency condition. The depth of Shin-gumho station is 43.6m which consists of the island-type platform ($8^{th}$ floor in underground) and a two-story lobby (first & second floor in underground). An emergency staircase connects between the platform and the lobby. Hot-wire anemometer, capture hood, wind vane & velocity meter and data acquisition systems are employed to perform the automatic measurement in this experiment. For ordinary case, air supply and exhaust capacity in the lobby were reduced by 34% and 46% compared to the original capacity, respectively. Air supply and exhaust capacity in the platform were reduced by 66% and 38%, respectively. For emergency case, air supply in the lobby was reduced by 42% and air exhaust in the platform was reduced by 28% compared to the original capacity. Therefore, air pollution in the station is expected to be worse in the ordinary environment and smoke control capability in the platform will be weakened in case of fire emergency.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.4
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• pp.301-306
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• 2011

This study was determined to analysis the characteristics of water mass in the Bottol Bada, Yeosu in August, 2010 based on the data from the distribution of water temperature and salinity. Sampling was carried out a total of three times (i.e. July 29, August 13, and August 30, 2010) and performed at three stations. Observation was done during the period of time 10:00-15:00, indicating the decreasing tidal height and turn of tide. In July 29, thermocline was found at 4 m in St. 1, but the stratification did not observe in August 13 and August 30. The remarkable water temperature between surface and bottom was found in St. 2 and St. 3, whereas St. 1 did not find. A particular finding during this study showed a cold water mass at bottom layer from St. 2 and St. 3, which was first occurred in July 29 and persisted in August 30 without any of destruction. Water temperature had a remarkable fluctuation between surface and bottom, whereas salinity had a unique in St. 1. St. 2 and St. 3 showed the increasing salinity according to water depth in August 13 and August 30. Transparency had considerable fluctuations in St. 1 and St. 3 depending to sampling date, but St. 2 did not fluctuate. Consequently, the Bottol Bada had a significantly different water mass between inner and outer waters. Furthermore, strong irradiance and weak wind play an important role in developing the stratification between surface and bottom, in particular the introduction of offshore waters contribute to highly developing the stratification in the Bottol Bada during the period of August in 2010.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.102-115
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• 2011

The purpose of this study is to establish the evaluation model through the systematic process of selecting the indicators and to evaluate the environmental-control function on forest using GIS in Deagu for the sustainable forest planning. The 35 indicators as basic items were selected by literature review and those were squeezed into the 29 indicators through expert brainstorming. Also, the 8 indicators to evaluate environmental-control function were selected by the first survey and the 5 final indicators such as carbon sink, temperature decrease, wind formation, water circulation, air purification were determined by MCB analysis using the second survey. The evaluation model was established through the weight of each indicator by AHP analysis using the third survey. According to the result of evaluating the environmental-control function on forest, the functions around the top area of Mt. Ap, Mt. Biseul, Mt. Palgong had more than 66 scores. On the other hand, the functions around Mt. Waryong and forest of Chilgok in Buk-gu had less than 40 scores. It is necessary to improve the function through the sustainable restoration and management in case of forest that the environmental-control function was lower. Furthermore, these results will be able to be utilized as basic data in order to establish the preservation area and control development area at the urban, environmental, and forest planning.