• Journal of Digital Convergence
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• v.14 no.6
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• pp.457-466
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• 2016

To achieve eco-friendly hospitals it is necessary to empirically verify the effect of meteorological factors on the power consumption of the hospital. Using daily meteorological big data from 2009 to 2013, we studied the weather conditions impact to power consumption and analyzed the patterns of power consumption of two hospitals. R analysis revealed that temperature among the meteorological factors had the greatest impact on the hospital power consumption, and was a significant factor regardless of hospital size. The pattern of hospital power consumption differed considerably depending on the hospital size. The larger hospital had a linear pattern of power consumption and the smaller hospital had a quadratic nonlinear pattern. A typical pattern of increasing power consumption during a hot summer and a cold winter was evident for both hospitals. The results of this study suggest that a hospital's functional specificity and meteorological factors should be considered to improve energy savings and eco-friendly building.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.295-305
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• 2021

In this study, we constructed using Random Forest(RF) by selecting the meteorological factors related to the occurrence of frost. As a result, when constructing a classification model for frost occurrence, even if the amount of data set is large, the imbalance in the data set for development of model has been analyzed to have a bad effect on the predictive power of the model. It was found that building a single integrated model by grouping meteorological factors related to frost occurrence by region is more efficient than building each model reflecting high-importance meteorological factors. Based on our results, it is expected that a high-accuracy frost occurrence prediction model will be able to be constructed as further studies meteorological factors for frost prediction.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2003.04a
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• pp.74-78
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• 2003

작물은 환경의 영향, 특히 기상조건과는 밀접한 관계가 있으며, 많은 학자들이 기상과 작물의 생육에 대한 연구결과를 보고하였다. (Kwon 등, 1989, 1993, 1994, cho등, 1979, 1984, Lee,등, 1982, Park등, 1975 ; Won등, 1983). 본 실험에서는 여수 지역의 고추수량과 기상과의 관계에 대한 기초 자료뿐만 아니라 1991년부터 2000년 까지의 기상 환경과 고추의 수량관계를 분석한 결과는 다음과 같다. 1. 월별 기상요인중 5월의 평균기온이 25.0%로 가장 높았고, 최고기온이 7.1%, 최저기온이 8.8%로 각각 높았으며, 8월의 평균기온이 6.6%, 최고기온이 6,2%, 최저기온이 6,9%로 각각 비교적으로 낮아서 변이가 적었다. 2. 생육 및 수량형질의 변이 계수에서 수량은 13.57%로 매우 높아 품종고유의 유전특성의 지배를 적게 받는 반면, 경장은 9.55%로 높아서 어느정도 환경요인에 영향을 받는 것으로 나타났다. 3. 기상요인과 수량 및 수량구성형질 간에서는 5월의 최고기온과 초장, 과장, 과경, 수량간에는 정의상관으로 유의성이 높았으며, 고추의 개화수정기간인 8월의 강수량과 초장, 과장, 수량간에는 부의 상관으로 유의성이 나타나 초기생육기인 5월의 높은기온과 개화기간인 8월의 적은 강수량이 높은 수량을 올릴수 있었다. 4. 수량과 수량형질 간에는 모두 정의상관으로 높게 유의성을 나타내었었다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.116-118
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• 2011

해안가에 건설되는 구조물 표면은 해풍에 의해 직접적인 영향을 받는다. 해풍에 섞여 날아오는 염분이 건축물 외부에 흡착하여 시간이 경과함에 따라 내부로 확산 이동하게 되어, 콘크리트 내부의 철근이 부식하게 된다. 따라서, 해안가 구조물의 비래염분에 대한 열화 예측을 보다 정확하게 조사하기 위해서는 실환경하에 작용하는 기상요인, 지형, 구조물의 형태 등 대상지역의 다양한 조건을 정밀하게 반영하여 정해야 할 필요가 있다. 풍향, 풍속, 온도, 습도등의 다양하게 변화하는 기상요인에서 각 요인에 따라 발생되는 비래염분량과의 상관성을 분석하기 위해서는 고정된 실험인자에 따른 지속적인 실험이 필요하다. 따라서 본 연구는 비래염분 포집기 개발 및 인공비래염분 발생장치(이하, 인공장치) 개발과 장치 성능의 정확성 향상을 위한 기초 실험 실시에 목표를 하였고, 또한 개발된 비래염분 포집기를 실 환경에 설치하여 각 실험인자에 따른 실환경에서의 비래염분 포집량을 분석하고자 한다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.2
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• pp.163-169
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• 1989

Seasonal climatic factors associated with tobacco quality grade and production rate were analyzed. The degree of influence on yield distribution rate in high guality tobacco leaues was highly positive with the average temperature in early May, but negatively related to those in late May and early June. Positive correlations were noticed between the degree of influence and sunshine hours in Middle June, late June and late May in decrease order, while negative degree of influence was higher in early May than in late May, The order influenced by recipitation in a positive direction was early May, late May and middle May. Negative influence was noticed in middle and early June with a great degree.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.156-160
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• 2015

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1087-1095
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• 2015

Influenza, commonly known as "the flu", is an infectious disease caused by the influenza virus. We consider, in this paper, regression models as a prediction model of influenza disease. While most of previous researches use mainly the meteorological variables as a predictive variables, we consider social media information in the models. As a result, we found that the contributions of two-type of informations are comparable. We used the medical treatment data of influenza provided by Natioal Health Insurance Survice (NHIS) and the meteorological data provided by Korea Meteorological Administration (KMA). We collect social media information (twitter buzz amount) from Twitter. Time series model is also considered for comparison.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.647-659
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• 2017

Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.319-327
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• 2016

This research attempts an analysis that fuses the big data concerning weather variation and health care from January 1, 2011 to December 31, 2014; it gives the weather factor as to what kind of influence there is for the incidence of food poisoning, and also endeavors to be helpful regarding national health prevention. By using R, the Logistic and Lasso Logistic Regression were analyzed. The main factor germ generating the food poisoning was classified and the incidence was confirmed for the germ of bacteria and virus. According to the result of the analysis of Logistic Regression, we found that the incidence of bacterial food poisoning was affected by the following influences: the average temperature, amount of sunshine deviation, and deviation of temperature. Furthermore, the weather factors, having an effect on the incidence of viral food poisoning, were: the minimum vapor pressure, amount of sunshine deviation and deviation of temperature. This study confirmed the correlation of meteorological factors and incidence of food poisoning. It was also found out that even if the incidence from two causes were influenced by the same weather factor, the incidence might be oppositely affected by the characteristic of the germs.