• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.57-64
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, the forest fire danger rating system was developed to estimate forest fire risk by means of weather, topography, and forest type. Forest fires occurrence prediction needs to improve continually. Logistic regression and spatial analysis was used in developing the forest fire occurrence probability model. The forest fire danger index in accordance to the probability of forest fire occurrence was used in the classification of forest fire occurrence risk regions.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.3
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• pp.56-61
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• 2012

The most common deterioration cause of concrete structures over the world is chloride ions attacks. Thus, service life modeling of concrete is a crucial issue in civil engineering society. Many studies on the durability of concrete have been accomplished, however, it is not easy to review literatures about environmental analysis. Since the durability of concrete depends on the properties of the surface concrete. micro-climatic condition which influences on surface concrete realistically should be considered. This study is devoted to analysis the micro-climatic condition of concrete structures, based on the in-situ monitoring of weather in marine environment. The effect of degree of saturation on chloride diffusivity of concrete is also examined. It is expected that the result of this work should be available for the prediction of chloride profile of marine concrete.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.729-737
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• 2013

The environmental load of concrete pavement can be categorized by temperature and moisture loads, which mean temperature distribution, and drying shrinkage and creep in the concrete slab. In this study, a method calculating the environmental load essential to mechanistic design of airport concrete pavement was developed. First, target area and design slab thickness were determined. And, the concrete temperature distribution with slab depth was predicted by a pavement temperature prediction program to calculate equivalent linear temperature difference. The concrete drying shrinkage was predicted by improving an existing model to calculate differential shrinkage equivalent linear temperature difference considering regional relative humidity. In addition, the stress relaxation was considered in the drying shrinkage. Eventually, the equivalent linear temperature difference due to temperature and the differential shrinkage equivalent linear temperature difference due to moisture were combined into the total equivalent linear temperature difference as terminal environmental load. The environmental load of eight civilian and two military airports which represent domestic regional weather conditions were calculated and compared by the method developed in this study to show its application.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.4
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• pp.212-221
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• 2016

In this study, characteristics of swell-like high waves in the East Sea were analyzed using observed wave data and predicted meteorological data from the National Oceanic and Atmospheric Administration (NOAA). And, the wave prediction system using the data from the NOAA has been established. Furthermore, the applicability of the system has been verified by comparing the predicted results with the corresponding observed data. For some case, there were two times of wave height increase and the second increase occurred in a calm weather condition on the coast which might cause casualties. The direction of wave energy propagation was estimated from observed wave data in February, 2008. Through comparison between the direction of wave energy propagation and the meteorological data, it turns out that the second increase of waves is originated from the seas between Russia and Japan which is far from the East Sea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.938-945
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• 2004

On the relationship between the red tide occurrence and the oceanographic factors in the middle coastal area in the South Sea of Korea, the favorable oceanographic conditions for the red tide formation are considered as follows； the calm weather increases sea water temperature in summer and early-fall which the red tide occurs frequently, and the heavy precipitation brings some riverine water to ween: low salinity, high suspended solid, low phosphorus and high nitrogen, respectively. We decided the potential areas in the coastal zones vulnerable to the red tide occurrence based on the limited factors controlling the growth of phytoplankton. By using GIS through the overlap for three subject figures (phosphorus, nitrogen and suspended solids), it was founded that the potential areas are the Yeosu∼Dolsan coast, the Gamak bay, the Namhae coast, the Narodo coast, the Goheung and Deukryang bay. This result has very well coincided to the results of the satellite and in-situ data.

• Journal of Astronomy and Space Sciences
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• v.30 no.4
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• pp.255-267
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• 2013

In this research, the ground contact opportunity for the fictitious low lunar orbiter is analyzed to prepare for a future Korean lunar orbiter mission. The ground contact opportunity is basically derived from geometrical relations between the typical ground stations at the Earth, the relative positions of the Earth and Moon, and finally, the lunar orbiter itself. Both the cut-off angle and the orbiter's Line of Sight (LOS) conditions (weather orbiter is located at near or far side of the Moon seen from the Earth) are considered to determine the ground contact opportunities. Four KOMPSAT Ground Stations (KGSs) are assumed to be Korea's future Near Earth Networks (NENs) to support lunar missions, and world-wide separated Deep Space Networks (DSNs) are also included during the contact availability analysis. As a result, it is concluded that about 138 times of contact will be made between the orbiter and the Daejeon station during 27.3 days of prediction time span. If these contact times are converted into contact duration, the duration is found to be about 8.55 days, about 31.31% of 27.3 days. It is discovered that selected four KGSs cannot provide continuous tracking of the lunar orbiter, meaning that international collaboration is necessary to track Korea's future lunar orbiter effectively. Possible combinations of world-wide separated DSNs are also suggested to compensate for the lack of contact availability with only four KGSs, as with primary and backup station concepts. The provided algorithm can be easily modified to support any type of orbit around the Moon, and therefore, the presented results could aid further progress in the design field of Korea's lunar orbiter missions.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.1-9
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• 2020

Reservoir storage and water level information is essential for accurate drought monitoring and prediction. In particular, the agricultural drought has increased the risk of agricultural water shortages due to regional bias in reservoirs and water supply facilities, which are major water supply facilities for agricultural water. Therefore, it is important to evaluate the available water capacity of the reservoir, and it is necessary to determine the water surface area and water capacity. Remote sensing provides images of temporal water storage and level variations, and a combination of both measurement techniques can indicate a change in water volume. In areas of ungauged water volume, satellite remote sensing image acts as a powerful tool to measure changes in surface water level. The purpose of this study is to estimate of reservoir storage and level variations using satellite remote sensing image combined with hydrological statistical data and the Normalized Difference Water Index (NDWI). Water surface areas were estimated using the Sentinel-2 satellite images in Seosan, Chungcheongnam-do from 2016 to 2018. The remote sensing-based reservoir storage estimation algorithm from this study is general and transferable to applications for lakes and reservoirs. The data set can be used for improving the representation of water resources management for incorporating lakes into weather forecasting models and climate models, and hydrologic processes.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.581-584
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• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.