• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.2
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• pp.37-45
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• 2022

A simplified model using the lifetime distribution has been presented to estimate the Mean Residual Life (MRL) of rubble-mound breakwaters, which is not like a stochastic process model based on time-dependent history data to the cumulative damage progress of rubble-mound breakwaters. The parameters involved in the lifetime distribution can be easily estimated by using the upper and lower limits of lifetime and their likelihood that made a judgement by several experts taking account of the initial design lifetime, the past sequences of loads, and others. The simplified model presented in this paper has been applied to the rubble-mound breakwater with TTP armor layer. Wiener Process (WP)-based stochastic model also has been applied together with Monte-Carlo Simulation (MCS) technique to the breakwater of the same condition having time-dependent cumulative damage to TTP armor layer. From the comparison of lifetime distribution obtained from each models including Mean Time To Failure (MTTF), it has found that the lifetime distributions of rubble-mound breakwater can be very satisfactorily fitted by log-normal distribution for all types of cumulative damage progresses, such as exponential, linear, and logarithmic deterioration which are feasible in the real situations. Finally, the MRL of rubble-mound breakwaters estimated by the simplified model presented in this paper have been compared with those by WP stochastic process. It can be shown that results of the presented simplified model have been identical with those of WP stochastic process until any ages in the range of MTT F regardless of the deterioration types. However, a little of differences have been seen at the ages in the neighborhood of MTTF, specially, for the linear and logarithmic deterioration of cumulative damages. For the accurate estimation of MRL of harbor structures, it may be desirable that the stochastic processes should be used to consider properly time-dependent uncertainties of damage deterioration. Nevertheless, the simplified model presented in this paper can be useful in the building of the MRL-based preventive maintenance planning for several kinds of harbor structures, because of which is not needed time-dependent history data about the damage deterioration of structures as mentioned above.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.622-629
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• 2022

The study aim was to derive a stem taper equation for Phyllostachys pubescens, a type of bamboo in South Korea, and to develop a stem volume table. To derive the stem taper equation, three stem taper models (Max & Burkhart, Kozak, and Lee) were used. Since bamboo stalks are hollow because of its woody characteristics, the outer and inner diameters of the tree were calculated, and connecting them enabled estimating the tree curves. The results of the three equations for estimating the outer and inner diameters led to selection of the Kozak model for determining the optimal stem taper because it had the highest fitness index and lowest error and bias. We used the Kozak model to estimate the diameter of Phyllostachys pubescens by stem height, which proved optimal, and drew the stem curve. After checking the residual degree in the stem taper equation, all residuals were distributed around "0", which proved the suitability of the equation. To calculate the stem volume of Phyllostachys pubescens, a rotating cube was created by rotating the stem curve with the outer diameter at 360°, and the volume was calculated by applying Smalian's method. The volume of Phyllostachys pubescens was calculated by deducting the inner diameter calculated volume from the outer diameter calculated volume. The volume of Phyllostachys pubescens was only 20~30% of the volume of Larix kaempferi, which is a general species. However, considering the current trees/ha of Phyllostachys pubescens and the amount of bamboo shoots generated every year, the individual tree volume was predicted to be small, but the volume/ha was not very different or perhaps more. The significance of this study is the stem taper equation and stem volume table for Phyllostachys pubescens developed for the first time in South Korea. The results are expected to be used as basic data for bamboo trading that is in increasing public and industrial demand and carbon absorption estimation.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.1
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• pp.4038-4051
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• 1976

This study was intended to provide the basic design creteria for the refrigerated storage, and to estimate the required optimum capacity of refrigerator for the different sizes and kinds of the existing fruit storage. The structural characteristics of the existing fruit storages in Pyungtaek-khun of Kyungki-do were surveyed. The average out-door air temperature during the expected storage life after harvesting, was obtained by analyzing the weather information. The heat transfer rates through the different models of storage walls were estimated. The refrigerating load required for different models of fruit storage was analyzed in the basis of out-door air temperature. The results obtained in this study are summarized as follows: 1. The fruit storages surveyed were constructed on-ground, under-ground and sub-ground type buildings. The majority of them being the on-ground buildings are mostly made of earth bricks with double walls. Rice hull was mostly used as the insulating materials for their walls and ceilings. About 42% of the buildings were with the horizontal ceiling, 22% with sloped ceiling, and about 36% without ceiling. About 60% of the storage buildings had floor without using insulated material. They were made of compacted earth. 2. There is no difference in heat transfer among six different types of double walls. The double wall, however, gives much less heat transfer than the single wall. Therefore, the double wall is recommended as the walls of the fruit storage on the point of heat transfer. Especially, in case of the single wall using concrete, the heat transfer is about five time of the double walls. It is evident that concrete is not proper wall material for the fruit storage without using special insulating material. 3. The heat transfer through the storage walls is in inverse proportion to the thickness of rice hull which is mostly used as the insulating material in the surveyed area. It is recommended that the thickness of rice hull used as the insulating material far storage wall is about 20cm in consideration of the decreasing rate of heat transfer and the available storage area. 4. The design refrigerating load for the on-ground storages having 20 pyung area is estimated in 4.07 to 4.16 ton refrigeration for double walls, and 5.23 to 6.97 ton refrigeration for single walls. During the long storage life, however, the average daily refrigerating load is ranged from 0.93 to 0.95 ton refrigeration for double walls, and from 1.15 to 1.47 ton refrigeration for single walls, respectively. 5. In case of single walls, 50.8 to 61.4 percent to total refrigerating load during the long storage life is caused by the heat transferred into the room space through walls, ceiling and floor. On the other hand, 39.1 to 40.7 percent is for the double walls. 6. The design and average daily refrigerating load increases in linear proportion to the size of storage area. As the size increases, the increasing rate of the refrigerating load is raised in proportion to the heat transfer rate of the wall. 7. The refrigerating load during the long storage life has close relationship to the out-door air temperature. The maximum refrigeration load is shown in later May, which is amounted to about 50 percent to the design refrigerating load. 8. It is noted that when the wall material having high heat transfer rate, such as the single wall made of concrete, is used, heating facilities are required for the period of later December to early February.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.373-380
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• 2013

The study aimed to develop prediction models of primal cut yield using body measurements of Hanwoo steers in Korea. The progeny of 874 steers at Hanwoo Improvement Main Center from 2008 to 2010 were recorded. Pearson's correlation coefficients for primal cuts and other traits were estimated. Primal cuts were adjusted for slaughter date and age using the SAS GLM procedure. Afterwards, a stepwise regression was performed on each primal cut by fitting body measurement traits. An independent covariable was selected at the highest coefficient of determination with the greater fitness model using Mallows's Cp statistic. Results showed that primal cuts were significantly influenced by slaughter date (P<0.01). The age at slaughter, however, was only significant for the top round (P<0.05). There was a moderate to high correlation between chest girth and tenderloin (0.54), loin (0.74), and rib (0.80). Most primal cut percentages were negatively related to BFT. Similar negative to low positive correlations were observed for primal cut percentage and body size traits. In addition, a correlation of 0.21 was observed between rib percentage and chest girth. The regression of body measurements on the adjusted primal cuts were significant for later traits. Regression estimates revealed that wither height, body length, rump length, hip bone width, and chest girth are important for primal cut weight and percentage determination. In particular, chest girth was always important for primal cut weight estimates.

• The Korean Journal of Nuclear Medicine
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• v.32 no.1
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• pp.32-49
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• 1998

For estimation of regional myocardial blood flow with O-15 water PET, a few modifications considering partial volume effect based on single compartment model have been proposed. In this study, we attempted to quantify the degree of heterogeneity and to show the effect of tissue flow heterogeneity on partition coefficient(${\lambda}$) and to find the relation between perfusable tissue index(PTI) and ${\lambda}$ by computer simulation using two modified models. We simulated tissue curves for the regions with homogeneous and heterogeneous blood flow over a various flow range(0.2-4.0ml/g/min). Simulated heterogeneous tissue composed of 4 subregions of the same or different size of block which have different homogeneous flow and different degree of slope of distribution of blood flow. We measured the index representing heterogeneity of distribution of blood flow for each heterogeneous tissue by the constitution heterogeneity(CH). For model I, we assumed that tissue recovery coefficient ($F_{MME}$) was the product of partial volume effect($F_{MMF}$) and PTI. Using model I, PTI, flow, and $F_{MM}$ were estimated. For model II, we assumed that partition coefficient was another variable which could represent tissue characteristics of heterogeneity of flow distribution. Using model II, PTI, flow and ${\lambda}$ were estimated. For the simulated tissue with homogeneous flow, both models gave exactly the same estimates, of three parameters. For the simulated tissue with heterogeneous flow distribution, in model I, flow and $F_{MM}$ were correctly estimated as CH was increased moderately. In model II, flow and ${\lambda}$ were decreased curvi-linearly as CH was increased. The degree of underestimation of ${\lambda}$ obtained using model II, was correlated with CH. The degree of underestimation of flow was dependent on the degree of underestimation of ${\lambda}$. PTI was somewhat overestimated and did not change according to CH. We conclude that estimated ${\lambda}$ reflect the degree of tissue heterogeneity of flow distribution. We could use the degree of underestimation of ${\lambda}$ to find the characteristic heterogeneity of tissue flow and use ${\lambda}$ to recover the underestimated flow.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.8
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• pp.870-876
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• 2005

The multiple regression models which have two factors(population and commercial area) have been used to forecast the water demand in the future. But, the coefficient of population had a negative value because proper regional classification wasn't performed, and it is not reasonable because the population must be a positive factor. So, the regional classification was performed by principal component and cluster analysis to solve the problem. 6 regional characters were transformed into 4 principal components, and the areas were divided into two groups according to cluster analysis which had 4 principal components. The new regression models were made by each group, and the problem was solved. And, the future water demands were estimated by three scenarios(Active, moderate, and passive one). The increase of water demand ore $89.034\;m^3/day$ in active plat $49,077\;m^3/day$ in moderate plan, and $19,996\;m^3/day$ in passive plan. The water supply ability as scenarios is enough in water treatment plant, however, 2 reservoirs among 4 reservoirs don't have enough retention time in all scenarios.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.2
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• pp.47-61
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• 2000

Agroclimatic zoning was done for paddy rice culture in North Korea based on a simulation experiment. Daily weather data for the experiment were generated by 3 steps consisting of spatial interpolation based on topoclimatological relationships, zonal summarization of grid cell values, and conversion of monthly climate data to daily weather data. Regression models for monthly climatological temperature estimation were derived from a statistical procedure using monthly averages of 51 standard weather stations in South and North Korea (1981-1994) and their spatial variables such as latitude, altitude, distance from the coast, sloping angle, and aspect-dependent field of view (openness). Selected models (0.4 to 1.6$^{\circ}C$ RMSE) were applied to the generation of monthly temperature surface over the entire North Korean territory on 1 km$\times$l km grid spacing. Monthly precipitation data were prepared by a procedure described in Yun (2000). Solar radiation data for 27 North Korean stations were reproduced by applying a relationship found in South Korea ([Solar Radiation, MJ m$^{-2}$ day$^{-1}$ ] =0.344 + 0.4756 [Extraterrestrial Solar Irradiance) + 0.0299 [Openness toward south, 0 - 255) - 1.307 [Cloud amount, 0 - 10) - 0.01 [Relative humidity, %), $r^2$=0.92, RMSE = 0.95 ). Monthly solar irradiance data of 27 points calculated from the reproduced data set were converted to 1 km$\times$1 km grid data by inverse distance weighted interpolation. The grid cell values of monthly temperature, solar radiation, and precipitation were summed up to represent corresponding county, which will serve as a land unit for the growth simulation. Finally, we randomly generated daily maximum and minimum temperature, solar irradiance and precipitation data for 30 years from the monthly climatic data for each county based on a statistical method suggested by Pickering et a1. (1994). CERES-rice, a rice growth simulation model, was tuned to accommodate agronomic characteristics of major North Korean cultivars based on observed phenological and yield data at two sites in South Korea during 1995~1998. Daily weather data were fed into the model to simulate the crop status at 183 counties in North Korea for 30 years. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to score the suitability of the county for paddy rice culture.