• 농업과학연구
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• 제41권4호
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• pp.461-472
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• 2014

This study was conducted to build the CFD simulation model which can quantify the distribution of the meteorological factors in air-heated greenhouse for chrysanthemum according to the location and capacity of air-circulation fan. The CFD model was also verified by experiment. It was judged that SST model was the most appropriate turbulence model which can properly describe the airflow by the air-circulation fan. According to the simulation results, the differences between the measured and predicted temperatures from 18 points at each height in the greenhouse were $0.2{\sim}0.4^{\circ}C$ in average. This showed a good agreement between the predicted data and the measured ones. The developed CFD model can be a useful tool to evaluate and design the air-circulation systems in the greenhouse with various configurations.

• 응용통계연구
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• 제30권3호
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• pp.417-426
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• 2017

국내외적으로 벚꽃의 개화시기와 단풍 시작일에 대한 관련 연구가 많이 되어왔는데, 매해 기상청은 이들에 대하여 보도자료를 통하여 당해 연도 예측시기 및 전년도와의 차이를 발표하고 있다. 본 연구에서는 선형회귀모형을 통해서 이들의 개화시기 및 단풍시기에 영향을 줄 수 있는 월별 평균, 최저, 최고온도, 강수량 그리고 일조량 등과 같은 여러가지 기상변수를 확인하였고 관측되지 않은 지역에 대한 예측을 위해서 기존의 모형에 공간구조를 추가하여 분석하였다. 본 연구에서 제안된 모형을 2009년부터 2016년의 기상청 보도자료에 적용하였고 기존의 기상청 예측값과 비교하였다. 그리고 공간분석을 통해 한반도 전역의 벚꽃 개화시기와 단풍 시작일을 예측하였다.

• 한국환경과학회지
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• 제19권7호
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• pp.829-841
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• 2010

The estimation of a biogenic volatile organic compound (BVOC, especially isoprene) and the influence of isoprene emissions on ozone concentrations in the Greater Busan Area (GBA) were carried out based on a numerical modeling approach during a high ozone episode. The BVOC emissions were estimated using a biogenic emission information system (BEIS v3.14) with vegetation data provided by the forest geographical information system (FGIS), land use data provided by the environmental geographical information system (EGIS), and meteorological data simulated by the MM5. Ozone simulation was performed by two sets of simulation scenarios: (1) without (CASE1) and (2) with isoprene emissions (CASE2). The isoprene emission (82 ton $day^{-1}$) in the GBA was estimated to be the most dominant BVOC followed by methanol (56) and carbon monoxide (28). Largest impacts of isoprene emissions on the ozone concentrations (CASE2-CASE1) were predicted to be about 4 ppb in inland locations where a high isoprene was emitted and to be about 2 ppb in the downwind and/or convergence regions of wind due to both the photochemical reaction of ozone precursors (e.g., high isoprene emissions) and meteorological conditions (e.g., local transport).

• 한국태양에너지학회 논문집
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• 제30권6호
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• Journal of Ecology and Environment
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• 제44권2호
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• pp.98-105
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• 2020

Background: The purpose of this study, mosquito forecast system implemented by Seoul Metropolitan city, was to obtain the mosquito prediction formula by using the mosquito population data and the environmental data of the past. Results: For this study, the mosquito population data from April 1, 2015, to October 31, 2017, were collected. The mosquito population data were collected from the 50 smart mosquito traps (DMSs), two of which were installed in each district (Korean, gu) in Seoul Metropolitan city since 2015. Environmental factors were collected from the Automatic Weather System (AWS) by the Korea Meteorological Administration. The data of the nearest AWS devices from each DMS were used for the prediction formula analysis. We found out that the environmental factors affecting the mosquito population in Seoul Metropolitan city were the mean temperature and rainfall. We predicted the following equations by the generalized linear model analysis: ln(Mosquito population) = 2.519 + 0.08 × mean temperature + 0.001 × rainfall. Conclusions: We expect that the mosquito forecast system would be used for predicting the mosquito population and to prevent the spread of disease through mosquitoes.

• 농업과학연구
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• 제46권1호
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of Radiation Protection and Research
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• 제25권2호
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• pp.75-80
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• 2000

월성원전 주변에서 실측된 공기중 및 솔잎중 삼중수소 농도와 INDAC 코드 예측치의 비교 평가를 수행하였다. 또한, 방사성물질의 환경중 방출유형, 풍속분류, 지형 고려 유무 등에 따른 삼중수소 예측치의 변화를 측정치와 비교 평가함으로써, 규제실무에 적용할 수 있는 최적 기상자료 입력방안을 도출하였다 공기중 및 솔잎중 삼중수소 농도와 INDAC 코드 예측치의 비교평가 결과, 월성원전 주변 삼중수소 농도 예측치는 측정값의 불착실성, 지현의 복잡성, 해륙풍의 영향 등의 제한점에도 불구하고 규제검증에 필요한 정도의 보수성을 유지하면서도 삼중수소의 거동을 잘 모사함을 확인할 수 있었다. 또한, INDAC 코드에 적용된 솔잎중 삼중수소 농도의 평가방법론 즉, 대기내 수분중의 삼중수소 농도의 절반이 식물체내에 골고루 분포되어 있다고 가정하는 것은 규제 검증에 필요한 보수성을 확보하고 있음을 알 수 있었다. 최적 기상자료 입력방안의 도출을 위한 민감도 분석 결과, 혼합방출 및 지형을 고려하지 않는 경우에는 실측치 대비 과소 평가되는 부분이 있어 규제실무에 적용하기에는 적합하지 않은 것으로 나타났다. 따라서, 혼합방출을 고려하는 경우에는 58m 기상자료와 지형자료에 근거하여 대기확산인자를 평가하여야 하는 것으로 나타났다.

• 한국조경학회지
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• 제34권1호
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• pp.21-36
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• 2006

This study was undertaken to analyze the effects of pavement thermal properties and water retention characteristics on the surface temperature of the gray permeable cement concrete pavement during the summer. Following is a summary of major results. 1) The hourly surface temperature of pavement could be well predicted with a heat transfer model program that incorporated the input data of major meteorological variables including solar radiation, atmospheric temperature, dew point, wind velocity, cloudiness and the evaporation rate of the pavements predicted by the time domain reflectometry (TDR) method. 2) When the albedo was changed to 0.5 from an arbitrary starting condition of 0.3, holding other variables constant, the peak surface temperature of the pavement dropped by 11.5%. When heat capacity was changed to $2.5\;kJm^{-3}K^{-1}\;from\;1.5\;kJm^{-3}K^{-1}$, surface temperature dropped by 8.0%. When daily evaporation was changed to 1 mm from 2 mm, temperature dropped by 5.7%. When heat conductivity was changed to $2.5\;Wm^{-1}K^{-1}\;from\;1.5\;Wm^{-1}K^{-1}$, the peak surface temperature of the pavement fell by 1.2%. The peak pavement surface temperature under the arbitrary basic condition was $24.46^{\circ}C$ (12 a.m.). 3) It accordingly became evident that the pavement surface temperature can be most effectively lowered by using materials with a high albedo, a high heat capacity or a high evaporation at the pavement surface. The glare situation, however, is intensified by raising of the albedo, moreover if reflected light is absorbed into surrounding physical masses, it is changed into heat. It accordingly became evident that raising the heat capacity and the evaporative capacity may be the moot acceptable measures to improve the thermal characteristics of the pavement. 4) The sensitivity of the surface temperature to major meteorological variables was as follows. When the daily average temperature changed to $0^{\circ}C\;from\;15^{\circ}C$, holding all other variables constant, the peak surface temperature of the pavement decreased by 56.1 %. When the global solar radiation changed to $200\;Wm^{-2}\;from\;600\;Wm^{-2}$, the temperature of the pavement decreased by 23.4%. When the wind velocity changed to $8\;ms^{-1}\;from\;4\;ms^{-1}$, the temperature decreased by 1.4%. When the cloudiness level changed to 1.0 from 0.5, the peak surface temperature decreased by 0.7%. The peak pavement surface temperature under the arbitrary basic conditions was $24.46^{\circ}C$ (12 a.m.)

• 대한원격탐사학회지
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• 제36권6_3호
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• pp.1711-1720
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• 2020

최근 급속한 산업화와 도시화로 인해 인위적으로 발생하는 미세먼지(Particulate matter, PM)는 기상 조건에 따라 이동 및 분산되면서 피부와 호흡기 등 인체에 악영향을 미친다. 본 연구는 기상인자를 multiple linear regression(MLR), support vector machine(SVM), 그리고 random forest(RF) 모델의 입력자료로 하여 서울시 PM10 농도를 예측하고, 모델 간 성능을 비교 평가하는데 그 목적을 둔다. 먼저 서울시에 소재한 39개소 대기오염측정망(air quality monitoring sites, AQMS)에서 관측된 PM10 농도 자료를 8:2 비율로 구분하여 모델 훈련과 검증 데이터셋으로 사용되었다. 또한 기상관측소(automatic weather system, AWS)에서 관측되고 있는 자료 중 9개 기상인자(평균기온, 최고기온, 최저기온, 일 강수량, 평균풍속, 최대순간풍속, 최대순간풍속풍향, 황사발생유무, 상대습도)가 모델의 입력자료로 선정되었다. 각 AQMS에서 관측된 PM10 농도와 MLR, SVM, 그리고 RF 모델에 의해 예측된 PM10 농도 간 결정계수(R2)는 각각 0.260, 0.772, 그리고 0.793이었고, RF 모델이 PM10 농도 예측에 가장 높은 성능을 나타냈다. 특히 모델 검증에 사용되는 AQMS 중 관악구와 강남대로 AQMS는 상대적으로 AWS에 가까워 SVM과 RF 모델에서 높은 정확도를 나타냈다. 종로구 AQMS는 AWS에서 비교적 멀리 떨어져 있지만, 인접한 두 AQMS 데이터가 모델 학습에 사용되었기 때문에 두 모델에서 높은 정확도를 나타냈다. 반면 용산구 AQMS는 AQMS 및 AWS에서 비교적 멀리 떨어져 있기에 두 모델의 성능이 낮게 나타냈다.

• 농촌지도와개발
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• 제22권4호
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• pp.423-434
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• 2015

This paper predicts the onion's cultivation areas, yields per unit area, and wholesale prices during ship dates by using wholesale price data from the Korea Agro-Fisheries & Food Trade Corporation, the production data from the Statistics Korea, and the weather data from the Korea Meteorological Administration with an ARDL model. By analyzing the data of wholesale price, rural household income and rural total earnings, onion cultivation areas in 2015 are estimated to be 21,035, 17,774 and 20,557(ha). In addition, onion yields per unit area of South Jeolla Province, North Gyeongsang Province, South Gyeongsang Province, Jeju Island, and the whole country in 2015 are estimated to be 5,980, 6,493, 6,543, 6,614, 6,139 (kg/10a) respectively. By using onion production's predictive value found from onion's cultivation areas and yields per unit area in 2015, the onion's wholesale prices in June are estimated to be 780 won, 1,100 won, and 820 won for each model. Predicted monthly price after the onion's ship dates is analyzed to exceed 1,000 won after August.