• Journal of Environmental Science International
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• v.27 no.7
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.

• Atmosphere
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• v.32 no.2
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• pp.149-162
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• 2022

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.3
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• pp.9-19
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• 2015

Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.63-71
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• 2021

Windbreak forests, which have a windproof effect against strong winds, are known to be effective in reducing the spread of odors and dust emitted from livestock farms. The effect of reducing the spread of odors and dust can be estimated through numerical models such as computational fluid dynamics, which require aerodynamic coefficients of the windbreaks for accurate prediction of their performance. In this study, we aimed to evaluate the aerodynamic coefficients, Co, C₁, C₂, and α, of two windbreaks, Thuja occidentalis and a mesh net, through wind tunnel experiments. The aerodynamic coefficients were derived by the relation between the incoming wind speed and the pressure loss due to the windbreaks which was measured by differential pressure sensors. In order to estimate the change in the aerodynamic coefficient concerning various leaf density, the experiments were conducted repeatedly by removing the leaves gradually in various stages. The results showed that the power law regression model more suitable for coefficient evaluation compared to the Darcy-Forchheimer model.

• Journal of Ocean Engineering and Technology
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• v.37 no.5
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• pp.181-189
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• 2023

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

• Korean Journal of Computational Design and Engineering
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• v.17 no.1
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• pp.45-53
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• 2012

In this paper, oil spreading simulation model is proposed for analyzing the oil spreading phenomenon rapidly when the ocean is polluted by the oil from a stranded ship. The space occupied by the ocean is converted into the latticed cell, and the each cell contains the information, such as the quantity of the oil, the temperature of the ocean, and the direction of current and wind. Two states, such as "clean" and "polluted" are defined in the each cell, and the oil in the cell spreads to the neighbor cells by the spreading rules. There are three spreading rules. First, the oil in the certain cell only spreads to the neighbor cells that contain larger oil than the certain cell. Second, the oil evaporates in proportion to the temperature of the ocean at the every time step. Third, the oil spreading property is affected by the direction and the speed of the current and the wind. The oil spreading simulation model of the each cell is defined by using the combined discrete event and discrete time simulation model architecture with the information and the spreading rules in the cell. The oil spreading simulation is performed when the oil of 10,000 kL is polluted in the ocean environment of 300 m by 300 m with various current and wind.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1395-1406
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• 2022

This study investigated the effects of the urban forest and street trees on flow and temperature distribution in the Daegu National Debt Redemption Movement Memorial Park. For this, we implemented tree-drag and tree-cooling parameterization schemes in a computational fluid dynamics (CFD) model and validated the simulated wind speeds, wind directions, and air temperatures against the measured ones. We used the wind speeds, wind directions, air temperatures predicted by the local data assimilation and prediction system (LDAPS) as the inflow boundary conditions. To investigate the flow and thermal characteristics in the presence of trees in the target area, we conducted numerical experiments in the absence and presence of trees. In the absence of trees, strong winds and monotonous flows were formed inside the park, because there were no obstacles inducing friction. The temperature was inversely proportional to the wind speed. In the presence of trees, the wind speeds(temperatures) were reduced by more than 40 (5)% inside the park with a high planting density due to the tree drag (cooling) effect, and those also affected the wind speeds and temperatures outside the park. Even near the roadside, the wind speeds and temperatures were generally reduced by the trees, but the wind speeds and air temperatures increased partly due to the change in the flow pattern caused by tree drag.

• Atmosphere
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• v.22 no.1
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• pp.47-55
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• 2012

The characteristics of flow and pollutant dispersion for fire scenarios in an urban area are numerically investigated. A computational fluid dynamics (CFD) model coupled to a mesoscale weather research and forecasting (WRF) model is used in this study. In order to more accurately represent the effect of topography and buildings, the geographic information system (GIS) data is used as an input data of the CFD model. Considering prevailing wind, firing time, and firing points, four fire scenarios are setup in April 2008 when fire events occurred most frequently in recent five years. It is shown that the building configuration mainly determines wind speed and direction in the urban area. The pollutant dispersion patterns are different for each fire scenario, because of the influence of the detailed flow. The pollutant concentration is high in the horse-shoe vortex and recirculation zones (caused by buildings) close to the fire point. It thus means that the potential damage areas are different for each fire scenario due to the different flow and dispersion patterns. These results suggest that the accurate understanding of the urban flow is important to assess the effect of the pollutant dispersion caused by fire in an urban area. The present study also demonstrates that CFD model can be useful for the assessment of urban environment.