• Asian Journal of Atmospheric Environment
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• v.5 no.1
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• pp.1-7
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• 2011

This study assesses the dispersion and emission rates of odor form industrial area source. CALPUFF and AERMOD Gaussian models were used for predicting downwind odor concentration and calculating odor emission rates. The studied region was Seobu industrial complex in Korea. Odor samples were collected five days over a year period in 2006. In-site meteorological data (wind direction and wind speed) were used to predict concentration. The BOOT statistical examination software was used to analyze the data. Comparison between the predicted and field sampled downwind concentration using BOOT analysis indicates that the CALPUFF model prediction is a little better than AERMOD prediction for average downwind odor concentrations. Predicted concentrations of AERMOD model have a little larger scatter than that of CALPUFF model. The results also show odor emission rates of Seobu industrial complex area were an order of 10 smaller than that of beef cattle feed lots.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of the Korean Society for Environmental Technology
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• v.18 no.2
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• pp.165-172
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• 2017

In current stage, it is hard to predict the scale of damage caused by natural disaster and it is hard to deal with it. However, in case of disaster planning level, if it is possible to predict the scale of disaster then quick reaction can be done which will reduce the damage. In the present study, therefore, function of wind wave damage estimation among various disaster is developed. Damage of wind wave and typhoon in eastern and Jeju coastal zone was collected from disaster report (1991~2014) published by Ministry of Public Safety and Security and to reflect inflation rate, 2014 damage cost was converted. Also, wave height, wind speed, wave direction, wave period, etc was collected from Meteorological Administration and Korea Hydrographic and Oceanographic Administration web site. To reflect the characteristic of coastal zone when wave damage occurs, CODI(Coastal Disaster Index), COSI(Coastal Sensitivity Index), CPII(Coastal Potential Impact Index) published by Korea Hydrographic and Oceanographic Agency in 2015 were used. When damage occurs, function predicting wind wave damage was developed through weather condition, regional characteristic index and correlation of damage cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1581-1586
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• 2016

In this paper, we propose a central disaster monitoring system of the forest fire. This system provides the safe-zone and detection to reduce the suppression efforts. In existing system, it has a few providing the predicting of wildfire spread model and speed through topography, weather, fuel factor. This paper focus on the forest fire diffusion model and predictions of the path identified to ensure the safe zone. Also we have considering the forest fire of moving direction and speed for fire suppression and monitering. The proposed algorithm could provide the technique to analyze the attribute information that temperature, wind, smoke measured over time. This proposed central observing monitoring system could provide the moving direction of spred out forecast wildfire. This observing and monitering system analyze and simulation for the moving speed and direction forest fire, it could be able to predict and training the forest fire fighters in a given environment.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.87-96
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• 2011

In this study, the AERMOD dispersion model was used for predicting odor concentrations and back-calculating industrial area source odor emission rate. The studied area was Sihwa industrial complex in Korea. Odor samples were collected during two days over a year period in 2009. The comparison between the predicted and observed concentrations indicates that the AERMOD model could fairly well predict average downwind odor concentrations. The results show odor emission rates of Sihwa industrial complex area source were ranged from 0.204 to 2.320 $OUms^{-1}$ (average 0.476 $OUms^{-1}$). The results also show wind speed and direction are important parameters to the odor dispersion.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• Journal of the Korean earth science society
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• v.32 no.4
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• pp.360-372
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• 2011

In order to clarify the advection and dispersion characteristics of volcanic tephra to be emitted from the Mt. Baekdu, several numerical experiments were carried out using three-dimensional atmospheric dynamic model, Weather and Research Forecast (WRF) and Laglangian particles dispersion model FLEXPART. Four different temporally averaged meteorological values including wind speed and direction were used, and their averaged intervals of meteorological values are 1 month, 10 days, and 3days, respectively. Real time simulation without temporal averaging is also established in this study. As averaging time of meteorological elements is longer, wind along the principle direction is stronger. On the other hands, the tangential direction wind tends to be clearer when the time become shorten. Similar tendency was shown in the distribution of volcanic tephra because the dispersion of particles floating in the atmosphere is strongly associated with wind pattern. Wind transporting the volcanic tephra is divided clearly into upper and lower region and almost ash arriving the Korean Peninsula is released under 2 km high above the ground. Since setting up the temporal averaging of meteorological values is one of the critical factors to determine the density of tephra in the air and their surface deposition, reasonable time for averaging meteorological values should be established before the numerical dispersion assessment of volcanic tephra.

• Journal of Environmental Health Sciences
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• v.16 no.2
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• pp.1-10
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• 1990

This study was performed to evaluate a applicability of TCM(Texas Climatological Model) model to a industrial area sush as CUNSAN and a possibility to provide necessary informaitons for air quality management. The air pollutants were measured at 6 sampling sites of GUNSAN industrial area from june to july in 1989. The model was checked by comparing the observed data with estimated data. The meteorological data for wind direction and wind speed were obtained from the observatory station in GUNSAN. The results are summarized as follows. 1. Average concentrations of air pollutants at all sampling sites were SO$_{2}$ 0.011-0.019 ppm. NO$_{2}$ 0.012-0.017 ppm. CO 0.6-1.0 ppm. TSP 45.8-64.2 $\mu$g/m$^{3}$. 2. The emission amounts show that point source are in general higher than area source. 3. As a results of correlation analysis, relationship between SO$_{2}$ concentration in the observed value and estimated value showed positive significance.(r = 0.766) 4. The sulfer content of the 1.6% at present to 0.8%, which means a 53.3% reduction. By controlling stack height could be lowered 14.5%, but the effective way of emission control is use of the lower sulfer fuels than controlling stack height. 5. The ratio between SO$_{2}$ contration in the observed value and estimated value showed 1.05. There-fore, the TCM model was quite effective in predicting air quality in GUNSAN industrial area.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.595-610
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• 2003

The statistical indexes such as RMSE (Root Mean Square Error), Mean Bias error, and IOA (Index of agreement) are used to evaluate 3 Dimensional wind and temperature fields predicted by operational meteorological model RAMS (Regional Atmospheric Meteorological System) implemented in CARIS (Chemical Accident Response Information System) for the dispersion forecast of hazardous chemicals in case of the chemical accidents in Korea. The operational atmospheric model, RAMS in CARIS are designed to use GDAPS, GTS, and AWS meteorological data obtained from KMA (Korean Meteorological Administration) for the generation of 3-dimensional initial meteorological fields. The predicted meteorological variables such as wind speed, wind direction, temperature, and precipitation amount, during 19 ∼ 23, August 2002, are extracted at the nearest grid point to the meteorological monitoring sites, and validated against the observations located over the Korean peninsula. The results show that Mean bias and Root Mean Square Error are 0.9 (m/s), 1.85 (m/s) for wind speed at 10 m above the ground, respectively, and 1.45 ($^{\circ}C$), 2.82 ($^{\circ}C$) for surface temperature. Of particular interest is the distribution of forecasting error predicted by RAMS with respect to the altitude; relatively smaller error is found in the near-surface atmosphere for wind and temperature fields, while it grows larger as the altitude increases. Overall, some of the overpredictions in comparisons with the observations are detected for wind and temperature fields, whereas relatively small errors are found in the near-surface atmosphere. This discrepancies are partly attributed to the oversimplified spacing of soil, soil contents and initial temperature fields, suggesting some improvement could probably be gained if the sub-grid scale nature of moisture and temperature fields was taken into account. However, IOA values for the wind field (0.62) as well as temperature field (0.78) is greater than the 'good' value criteria (> 0.5) implied by other studies. The good value of IOA along with relatively small wind field error in the near surface atmosphere implies that, on the basis of current meteorological data for initial fields, RAMS has good potentials to be used as a operational meteorological model in predicting the urban or local scale 3-dimensional wind fields for the dispersion forecast in association with hazardous chemical releases in Korea.