• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.393-402
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• 2023

Drought, which has been increasing in frequency and magnitude due to recent abnormal weather events, poses severe challenges in various sectors. To address this issue, it is important to develop technologies for drought monitoring, forecasting, and response in order to implement effective measures and safeguard the ecological health of aquatic systems during water scarcity caused by drought. This study aimed to predict water quality fluctuations during drought periods by integrating the watershed model HSPF and the water quality model QUAL-MEV. The researchers examined the SPI and RCP 4.5 scenarios, and analyzed water quality changes based on flow rates by simulating them using the HSPF and QUAL-MEV models. The study found a strong correlation between water flow and water quality during the low flow. However, the relationship between precipitation and water quality was deemed insignificant. Moreover, the flow rate and SPI6 exhibited different trends. It was observed that the relationship with the mid- to long-term drought index was not significant when predicting changes in water quality influenced by drought. Therefore, to accurately assess the impact of drought on water quality, it is necessary to employ a short-term drought index and develop an evaluation method that considers fluctuations in flow.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.577-585
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• 2010

Traffic accidents are caused by several factors such as drivers, vehicles, and road environment. It is necessary to investigate and analyze them in advance to prevent similar and repetitive traffic accidents. Especially, the human factor is most significant element and traffic accidents by drinking-driving caused from human factor have become social problem to be paid attention to. The study analyzes traffic accidents resulting from drinking-driving and the effects of driver's attributes and environmental factors on them. The study is composed as two parts. First, the log-linear model is applied to analyze that accidents by drinking or non-drinking driving associate with road geometry, weather condition and personal characteristics. Probability is tested for drinking-driving accidents relative to non-drinking drive accidents. The study analyzes probability differences between genders, between ages, and between kinds of vehicles through odds multipliers. Second, traffic accidents related to drinking are classified into property damage, minor injury, heavy injury, and death according to their severity. Heavy injury is more serious than minor one and death is more serious than heavy injury. The ordinal regression models are established to find effecting factors on traffic accident severity.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• Journal of Bio-Environment Control
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• v.22 no.4
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• pp.371-377
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• 2013

The government has been carrying out a project for supporting the rain shelter for hot pepper as part of measures stabilizing the demand and supply of hot pepper since 2012. However, the eaves height of single-span plastic greenhouses extensively used in farms is low, which are inappropriate for the rainproof cultivation of hot pepper. This study attempted to develop single-span plastic greenhouses which are structurally safe and have the dimensions suitable for the rainproof cultivation of hot pepper as well. The structure status of plastic greenhouses and restructuring wishes of 56 rainproof cultivation farms nationwide were investigated to set up the width and height of the plastic greenhouses. 53% of the plastic greenhouses currently in operation had a width of under 7 m and 64% of their eaves had a height of 1.5 m or less, which accounted for the highest rate. Mostly the width of 7.0 m was desired for the greenhouses and the height of 2.0 m for their eaves, so these values were chosen as the dimensions for the singlespan plastic greenhouses. After an analysis of their structural safety while changing the specifications of the rafter pipe in various ways, 5 kinds of models were suggested considering the frame ratio and installation costs. The 12-Pepper-1 model is a developed single-span plastic greenhouse for hot pepper in which a ${\emptyset}42.2{\times}2.1t$ rafter pipe is installed at an interval of 90cm and the models of 12-Pepper-2 through 5 are the other developed ones in which a ${\emptyset}31.8{\times}1.5t$ rafter pipe is installed at intervals of 60 cm, 70 cm, 80 cm and 90 cm, respectively. As a result of an analysis of economic feasibility of 12-Pepper-2 compared to 10-Single-3 in the notification of the Ministry for Food, Agriculture, Forestry and Fisheries, it turned out that there would be an increase in profits by about 1.2 million won based on one building of a greenhouse sized 672 $m^2$.

• 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 Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.2
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• pp.89-103
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• 2013

National demands for the ocean forecasting system have been increased to support economic activity and national safety including search and rescue, maritime defense, fisheries, port management, leisure activities and marine transportation. Further, the ocean forecasting has been regarded as one of the key components to improve the weather and climate forecasting. Due to the national demands as well as improvement of the technology, the ocean forecasting systems have been established among advanced countries since late 1990. Global Ocean Data Assimilation Experiment (GODAE) significantly contributed to the achievement and world-wide spreading of ocean forecasting systems. Four stages of GODAE were summarized. Goal, vision, development history and research on ocean forecasting system of the advanced countries such as USA, France, UK, Italy, Norway, Australia, Japan, China, who operationally use the systems, were examined and compared. Strategies of the successfully established ocean forecasting systems can be summarized as follows: First, concentration of the national ability is required to establish successful operational ocean forecasting system. Second, newly developed technologies were shared with other countries and they achieved mutual and cooperative development through the international program. Third, each participating organization has devoted to its own task according to its role. In Korean society, demands on the ocean forecasting system have been also extended. Present status on development of the ocean forecasting system and long-term plan of KMA (Korea Meteorological Administration), KHOA (Korea Hydrographic and Oceanographic Administration), NFRDI (National Fisheries Research & Development Institute), ADD (Agency for Defense Development) were surveyed. From the history of the pre-established systems in other countries, the cooperation among the relevant Korean organizations is essential to establish the accurate and successful ocean forecasting system, and they can form a consortium. Through the cooperation, we can (1) set up high-quality ocean forecasting models and systems, (2) efficiently invest and distribute financial resources without duplicate investment, (3) overcome lack of manpower for the development. At present stage, it is strongly requested to concentrate national resources on developing a large-scale operational Korea Ocean Forecasting System which can produce open boundary and initial conditions for local ocean and climate forecasting models. Once the system is established, each organization can modify the system for its own specialized purpose. In addition, we can contribute to the international ocean prediction community.

• Journal of Bio-Environment Control
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• v.29 no.4
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• pp.414-420
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• 2020

This study was carried out to investigate the growth characteristics of standard chrysanthemum 'Baekma', such as fresh weight, dry weight, and leaf area and to develop prediction models for the production greenhouse based on the growth parameters and climatic elements. Sigmoid regressions models for the prediction of growth parameters in terms of dry weight and leaf area were analyzed according to the number of the day after transplanting and the accumulate temperature during this experimental period. The relative growth rate (RGR) of the chrysanthemum was 0.084 g·g^-1·d^-1 on average during the period.The dry weight and leaf area of 'Beakma' increased exponentially according to the number of day after transplanting and the accumulated temperature, in the case of dry weight increased by an average of 39.1% until 63 days (accumulated temperature of 1601℃), after that dry weight increased by an average of 7.4% before harvest. The leaf area increased by an average of 63.3% until the 28th day after transplanting, and by an average of 6.5% until the 84th day before flower bud differentiation occurred, and increased by an average of 10.6% before harvest. This experiment can be used as a useful data for establishing a cultivation management system and a planned year-round production system for standard chrysanthemum "Baekma". To make a more precise growth prediction model, it will need to be corrected and verified based on various weather data including accumulated irradiation.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.54-60
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• 2009

The purpose of this study is to analyze the impacts of three-dimensional land cover on changing urban air temperatures and to explore some strategies of urban landscaping towards mitigation of heat build-up. This study located study spaces within a diameter of 300m around 24 Automatic Weather Stations(AWS) in Seoul, and collected data of diverse variables which could affect summer energy budgets and air temperatures. The study also selected reflecting study objectives 6 smaller-scale spaces with a diameter of 30m in Chuncheon, and measured summer air temperatures and three-dimensional land cover to compare their relationships with results from Seoul's AWS. Linear regression models derived from data of Seoul's AWS revealed that vegetation volume, greenspace area, building volume, building area, population density, and pavement area contributed to a statistically significant change in summer air temperatures. Of these variables, vegetation and building volume indicated the highest accountability for total variability of changes in the air temperatures. Multiple regression models derived from combinations of the significant variables also showed that both vegetation and building volume generated a model with the best fitness. Based on this multiple regression model, a 10% increase of vegetation volume decreased the air temperatures by approximately 0.14%, while a 10% increase of building volume raised them by 0.26%. Relationships between Chuncheon's summer air temperatures and land cover distribution for the smaller-scale spaces also disclosed that the air temperatures were negatively correlated to vegetation volume and greenspace area, while they were positively correlated to hardscape area. Similarly to the case of Seoul's AWS, the air temperatures for the smaller-scale spaces decreased by 0.32% ($0.08^{\circ}C$) as vegetation volume increased by 10%, based on the most appropriate linear model. Thus, urban landscaping for the reduction of summer air temperatures requires strategies to improve vegetation volume and simultaneously to decrease building volume. For Seoul's AWS, the impact of building volume on changing the air temperatures was about 2 times greater than that of vegetation volume. Wall and rooftop greening for shading and evapotranspiration is suggested to control atmospheric heating by three-dimensional building surfaces, enlarging vegetation volume through multilayered plantings on soil surfaces.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.221-232
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• 2016

Models to estimate solar radiation have been used because solar radiation is measured at a smaller number of weather stations than other variables including temperature and rainfall. For example, solar radiation has been estimated using the Angstrom-Prescott (AP) model that depends on two coefficients obtained empirically at a specific site ($AP_{Choi}$) or for a climate zone ($AP_{Frere}$). The objective of this study was to identify the coefficients of the AP model for reliable estimation of solar radiation under a wide range of spatial and temporal conditions. A global optimization was performed for a range of AP coefficients to identify the values of $AP_{max}$ that resulted in the greatest degree of agreement at each of 20 sites for a given month during 30 years. The degree of agreement was assessed using the value of Concordance Correlation Coefficient (CCC). When $AP_{Frere}$ was used to estimate solar radiation, the values of CCC were relatively high for conditions under which crop growth simulation would be performed, e.g., at rural sites during summer. The statistics for $AP_{Frere}$ were greater than those for $AP_{Choi}$ although $AP_{Frere}$ had the smaller statistics than $AP_{max}$ did. The variation of CCC values was small over a wide range of AP coefficients when those statistics were summarized by site. $AP_{Frere}$ was included in each range of AP coefficients that resulted in reasonable accuracy of solar radiation estimates by site, year, and month. These results suggested that $AP_{Frere}$ would be useful to provide estimates of solar radiation as an input to crop models in Korea. Further studies would be merited to examine feasibility of using $AP_{Frere}$ to obtain gridded estimates of solar radiation at a high spatial resolution under a complex terrain in Korea.