• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.519-533
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• 2018

The sensible heat flux (SHF)and latent heat flux (LHF) over Korean Peninsula ocean during recent 4 years were calculated using Coupled Ocean-Atmosphere Response Experiment (COARE) 3.5 bulk algorithm and satellite-based atmospheric-ocean variables. Among the four input variables (10-m wind speed; U, sea surface temperature; $T_s$, air temperature; $T_a$, and air humidity; $Q_a$) required for heat flux calculation, Ta and $Q_a$, which are not observed directly by satellites, were estimated from empirical relations developed using satellite-based columnar atmospheric water vapor (W) and $T_s$. The estimated satellite-based $T_a$ and $Q_a$ show high correlation coefficients above 0.96 with the buoy observations. The temporal and spatial variability of monthly ocean heat fluxes were analyzed for the Korean Peninsula ocean. The SHF showed low values of $20W/m^2$ over the entire areas from March to August. Particularly, in July, SHF from the atmosphere to the ocean, which is less than $0W/m^2$, has been shown in some areas. The SHF gradually increased from September and reached the maximum value in December. Similarly, The LHF showed low values of $40W/m^2$ from April to July, but it increased rapidly from autumn and was highest in December. The analysis of monthly characteristics of the meteorological variables affecting the heat fluxes revealed that the variation in differences of temperature and humidity between air and sea modulate the SHF and LHF, respectively. In addition, as the sensitivity of SHF and LHF to U increase in winter, it contributed to the highest values of ocean heat fluxes in this season.

• Korean Journal of Soil Science and Fertilizer
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• v.15 no.4
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• pp.213-220
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• 1982

Soil water changes in lysimeters with four different soils and two different available soil depths were monitored during the growing seasons of the soybean-barley cropping from 1977 to 1980 in Suweon to evaluate evapotranspiration (ET) as a function of available soil water and evaporative demand of the atmosphere. ET was calculated with soil water profile and water balance. Soil water content was measured with a neutron moisture depth gauage and The evaporative demand of the atmosphere was estimated with a class A pan evaporation. Rainfall. solar radiation, and wind speed were observed to examine heat and water balances. The average ET of soybeans ranged from 1.6 mm/day at seedling to 6.5 mm/day at flowering, and that of barley ranged from 0.5 mm/day at the regrowth stage to 4.6 mm/day at heading; however, a large variability was observed. The ratio of ET to pan evaporation ($ET/E_o$) ranged from 0.5 to 1.1 for soybeans and 0.4 to 1.2 for barley. The soil evaporation factor ($K_e$) of the $ET/E_o$ component decreased as the soil water depleted and the canopy developed. The crop transpiration factor ($K_t$), another component of $ET/E_o$, also was a function of time and the soil water. $K_t$ was constant when the available soil water fraction (f) in the root zone was greater than a threshold value, and $K_e$ was decreased linearly when f was lower than this threshold. The threshold was 0.7 for the moderate evaporative demand days, 0.4 to 0.5 for the low evaporative demand days, and 0.9 to 0.96 for the high evaporative demand days. Conclusively, the ET can be estimated from the evaporative demand of the atmosphere, $E_o$, $K_e$ and $K_t$, and the available soil water content in the root zone.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.3
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• pp.202-210
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• 2013

As incomes increase, interest in ocean leisure picks up. As a result, a lot of research and developments on hull form design and production of planing boats, mostly used for ocean leisure, are needed. Analysis in researches on resistance of planing boats shows that resistance characteristic of planing boats is different from resistance characteristic of general boats because the former is fast, and its wetted surface is very small. Using Savitsky formula widely used in the calculation of effective horse power in shipbuildingyards, and propulsion system and engine manufacturers, this study calculated total resistance of a research planing boat. Then it analyzed the flow characteristics of the planing boat through theoretical analysis and wind tunnel experiment, and computed air resistance and lift force by changes of speed and trim angle. It also compared and analyzed result of theoretical analysis and experiment of the ratio of air resistance to total resistance under variations of velocity and trim angle. When the study is used to estimate more accurate effective horse power, it is expected to remedy abuses of unnecessarily installing high-powered engine. As nature disasters due to abnormal changes of weather increase, interest in greenhouse gas grows. International Maritime Organization (IMO) legislated Energy Efficiency Design Index (EEDI) and Energy Efficiency Operational Indicator (EEOI) to reduce ship greenhouse gas emissions. But this index will be applied to over 400 tons ships, small ships, emitting more greenhouse gases than larege ships per unit power, will dodge the regulations. Thus, this study indicated a problem by calculating greenhouse gas emissions of an ocean leisure planning boat (a small ship), and suggested the need for EEDI of small ships.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.1
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• pp.1-13
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• 2011

The Yellow Sea Cold Water (YSCW) is formed by cold and dry wind in the previous winter, and is known to spread southward along the central trough of the Yellow Sea in summer. Water characteristics of the YSCW and its movement in the northern East China Sea (ECS) are investigated by analyzing CTD (conductivity-Temperature-Depth) data collected from summertime hydrographic surveys between 2003 and 2009. By water mass analysis, we newly define the North Western Cold Water (NWCW) as a cold water mass observed in the study area. It is characterized by temperature below $13.2^{\circ}C$, salinity of 32.6~33.7 psu, and density (${\sigma}_t$) of 24.7~25.5. The NWCW appears to flow southward at about a speed less than 2 cm/s according to the geostrophic calculation. The newly defined NWCW shows an interannual variation in the range of temperature and occupied area, which is in close relation with the sea surface temperature (SST) over the Yellow Sea and the East China Sea in the previous winter season. The winter SST is determined by winter air temperature, which shows a high correlation with the winter-mean Arctic Oscillation (AO) index. The negative winter-mean AO causes the low winter SST over the Yellow Sea and the East China Sea, resulting in the summertime expansion and lower temperature of the NWCW in the study area. This study shows a dynamic relation among the winter-mean AO index, SST, and NWCW, which helps to predict the movement of NWCW in the northern ECS in summer.

• 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.