• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.533-538
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• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Journal of Biosystems Engineering
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• v.8 no.1
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• pp.61-69
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• 1983

A system dynamic model was developed to predict food grain production under the dynamic consideration of the production circumstance and inputs such as farm population, investment on agriculture, arable land, extensive technology and weather. By using the model, the variation of the food grain production from 1978 to 2008 was examined. The results of the model output says it is desirable that the persistent and long-term program should be studied to get necessary food grain production under the variational inputs and circumstances.

• Korean Journal of Environmental Biology
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• v.37 no.4
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• pp.759-768
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• 2019

This study was conducted to estimate the mean annual extreme minimum temperature raster and predict the potential distribution of the invasive plant, Ipomoea triloba, on the Korean peninsula. We collected annual extreme minimum temperature and mean coldest month minimum temperature data from 129 weather stations on the Korean peninsula from 1990-2019 and used this data to create a linear regression model. The min temperature of the coldest month raster from Worldclim V2 were used to estimate a 30 second spatial resolution, mean annual extreme minimum temperature raster of the Korean peninsula using a regression model. We created three climatic rasters of the Korean peninsula for use with the Proto3 species distribution model and input the estimated mean annual extreme minimum temperature raster, a Köppen-Geiger climate class raster from Beck et al. (2018), and we also used the mean annual precipitation from Worldclim V2. The potential distribution of I. triloba was estimated using the Proto3 model with 117 occurrence points. As a result, the estimated area for a potential distribution of I. triloba was found to be 50.7% (111,969 ㎢) of the Korean peninsula.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.129-136
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• 2012

Temperature of fresh concrete can be effectively used to predict the strength of concrete being cured and make an informed decision for stripping the molds. A hygrothermograph and thermo-couple sensors that require an extensive wiring have been applied to measure a temperature of concrete at the early stage of the curing process on site. However, these methods have limits to provide the temperature data in real time due to harsh working environment including frequent cutting of wires. Therefore, this study is aiming at developing a device based on wireless sensor network to measure the temperature of concrete being cured in formwork. The result showed that the wireless sensor with probe type thermistor which is developed had the same temperature data compared to the existed wire type thermistor, and we confirmed the temperature history of concrete in real time for 28 days throughout the gateway by wireless network that collects the temperature data measured from specimens in laboratory. Also, the network device for transmission can be easily separated from the probe sensor part and reused consistently. If the wireless sensor network device developed uses in the field, the temperature management of concrete will be systematically conducted from at the early stage of the curing, and especially be effective for cold weather concrete construction. In addition, it will contribute to the establishment of advanced quality control system for concrete and productivity of supervisors on site will be increased in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.2
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• pp.119-126
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• 1999

A numerical model using soil surface energy balance and soil heat flow equations to estimate mulched soil temperature was developed. The required inputs data include weather data, such as global solar radiation, air temperature, wind speed, atmospheric water vapor pressure, the optical properties of mulching material, and soil physical properties. The observed average soil temperature at 50 cm depth was used as the initial value of soil temperature at each depth. Soil temperature was simulated starting at 0 hour at an interval of 10 minutes. The model reliably described the variation of soil temperature with time progress and soil depth. The correlation between the estimated and measured temperature yielded coefficient values of 0.961, 0.966 for 5cm and 10cm depth of the bare soil, respectively, 0.969, 0.965 for the paper mulched soil, and 0.915, 0.938 for the black polyethylene film mulched soil. The percentages of absolute differences less than 2$^{\circ}$C between soil temperatures measured and simulated at 10 minute interval were 97.4% and 98.5% for 5 cm and 10cm for the bare soil, respectively, and 95.8% and 97.4% for the paper mulched soil, and 70.1% and 92.5% for the polyethylene film mulched soil. The results indicated that the model was able to predict the soil temperature fairly well under mulched condition. However, in the night time, the model performance was a little poor as compared with day time due to the difficulty of accurate determination of the atmospheric long wave radiation.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.14-26
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• 2016

This study analyzed the correlation between the standardized precipitation index(SPI) and forest fire occurrences using monthly accumulative rainfall data since 1970 and regional fire occurrence data since 1991. To understand the relationship between the SPI and forest fire occurrences, the correlations among the SPI of nine main observatory weather stations including Seoul, number of fire occurrences, and log of fire occurrences were analyzed. We analyzed the correlation of SPI with fire occurrences in the 1990s and 2000s and found that in the 1990s, the SPI of 3 months showed high correlation in Gyeonggi, Gangwon, and Chungnam, while the SPI of 6 months showed high correlation in Chungbuk, and the SPI of 12 months showed high correlation in Gyeongnam, Gyenongbuk, Jeonnam, and Jeonbuk. In the 2000s, the SPI of 6 months showed high correlation with the fire frequency in Gyeonggi, Chungnam, Chungbuk, Jeonnam, and Jeonbuk, whereas the fire frequency in western Gangwon was highly correlated with the SPI of 3 months and, in eastern Gangwon, Gyeongnam, and Gyenongbuk, with the SPI of 1 month. In the 1990s, distinct differences in the drought condition between the SPI of 3 months and 12 months in the northern and southern regions of Korean Peninsula were found, whereas the differences in both the SPI of 1 month and 6 months were found in the Baekdudaegan region except western Gangwon since the 2000s. Therefore, this study suggests that we can develop a model to predict forest fire occurrences by applying the SPI of 1-month and 6-month data in the future.

• Korean Journal of Agricultural Science
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• v.10 no.1
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• pp.118-128
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• 1983

Simulation model of natural air grain drying was discussed and modified to predict the changes of grain moisture content and dry matter loss of rough rice drying. The modified simulation model was then validated using actual test data. A series of simulated drying tests using official weather data for 15 years from Beaumont, Texas, was taken to make minimum airflow rate and maximum bed depth of rough rice drying by natural air, under different conditions of initial moisture content of rough rice, airflow rate and harvest date.

• Journal of Korea Water Resources Association
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• v.39 no.11 s.172
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• pp.923-934
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• 2006

In this study, the effects of variability in climate, groundwater withdrawal, and landuse on dry-weather streamflows were investigated by input sensitivity analysis using SWAT (Soil and Water Assessment Tool). Since only dry-period precipitation and daily average solar radiation among climate variables have high correlation coefficients to total flow (TF), sensitivity analyses of those were conducted. Furthermore, an equation was derived from simulation results for 30 years by multiple regression analysis. It may be used to estimate effects of various climatic variations (precipitation during the dry period, precipitation during the previous wet period, solar radiation, and maximum temperature). If daily average maximum temperatures increase, TFs during the dry period will decrease. Sensitivities of groundwater withdrawal and landuse were also conducted. Similarly, groundwater withdrawals strongly affect streamflow during the dry period. However, landuse changes (increasing urbanization) within the forested watershed do not appear to significantly affect TF during the dry period. Finally, a combined equation was derived that describes the relationship between the total runoff during the dry period and the climate, groundwater withdrawal and urban area proportion. The proposed equation will be useful to predict the water availability during the dry period in the future since it is dependent upon changes of temperature, precipitation, solar radiation, urban area ratio, and groundwater withdrawal.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.274-286
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• 2016

Carbon dioxide ($CO_2$) accounts for about 72% of the total greenhouse gas emissions. It is also widely known as a major cause of global warming. According to the IPCC's fifth evaluation report, the growth rate of atmospheric $CO_2$ has increased by 35% for the last 100 years and global warming is occurring much more rapidly than expected since 1990s. As a result of climate change, global warming is increasing the frequency and severity of extreme weather events around the world, which has changed forest vegetation zone and vegetation phenology. The Kyoto Protocol recognizes the importance of forests and refers to the conservation and enhancement of forests as sinks and reservoirs of greenhouse gases. In this regard, studies of tree responses to climate change are indispensable for predicting changes in the forest ecosystems in the future. Therefore, studies using long-term climate change research facilities, associated with long-term ecological research (LTER) in the fields, will make a considerable contribution to predict and approach the changes in the future.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.