• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.599-608
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• 2018

As natural disasters are increasing due to the unusual weather and the modern society is getting complicated, the rapid change of the urban environment has increased human disasters. Thus, citizens are becoming more anxious about social safety. The importance of preparation for safety has been suggested by providing the disaster safety services such as regional safety index, life safety map, and disaster safety portal application. In this paper, we propose an application framework to predict the urban safety index based on user's location with realtime weather/atmosphere data after creating a predication model based on the machine learning using number of occurrence cases and weather/atmosphere history data. Also, we implement an application to provide traffic safety index with executing preprocessing occurrence cases of traffic and weather/atmosphere data. The existing regional safety index, which is displayed on the Si-gun-gu area, has been mainly utilized to establish safety plans for districts vulnerable to national policies on safety. The proposed system has an advantage to service useful information to citizens by providing urban safety index based on location of interests and current position with realtime related data.

• 스마트미디어저널
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• 제8권1호
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• pp.82-91
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• 2019

This study provides the analysis and prediction of fruits diseases related to weather conditions (temperature, wind speed, solar power, rainfall and humidity) using Linear Model and Poisson Regression. The main goal of the research is to control the method of fruits diseases and also to prevent diseases using less agricultural pesticides. So, it is needed to predict the fruits diseases with weather data. Initially, fruit data is used to detect the fruit diseases. If diseases are found, we move to the next process and verify the condition of the fruits including their size. We identify the growth of fruit and evidence of diseases with Linear Model. Then, Poisson Regression used in this study to fit the model of fruits diseases with weather conditions as an input provides the predicted diseases as an output. Finally, the residuals plot, Q-Q plot and other plots help to validate the fitness of Linear Model and provide correlation between the actual and the predicted diseases as a result of the conducted experiment in this study.

• 한국미생물·생명공학회지
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• 제49권2호
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• pp.249-254
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• 2021

The study of the impact of weather on viral respiratory infections enables the assignment of causality to disease outbreaks caused by climatic factors. A better understanding of the seasonal distribution of viruses may facilitate the development of potential treatment approaches and effective preventive strategies for respiratory viral infections. We analyzed the incidence of human mastadenovirus infection using real-time reverse transcription polymerase chain reaction in 9,010 test samples obtained from Cheonan, South Korea, and simultaneously collected the weather data from January 1, 2012, to December 31, 2018. We used the data collected on the infection frequency to detect seasonal patterns of human mastadenovirus prevalence, which were directly compared with local weather data obtained over the same period. Descriptive statistical analysis, frequency analysis, t-test, and binomial logistic regression analysis were performed to examine the relationship between weather, particulate matter, and human mastadenovirus infections. Patients under 10 years of age showed the highest mastadenovirus infection rates (89.78%) at an average monthly temperature of 18.2℃. Moreover, we observed a negative correlation between human mastadenovirus infection and temperature, wind chill, and air pressure. The obtained results indicate that climatic factors affect the rate of human mastadenovirus infection. Therefore, it may be possible to predict the instance when preventive strategies would yield the most effective results.

• 한국항공운항학회지
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• 제29권4호
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• pp.1-20
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• 2021

Due to the recent rapid increase in passenger and cargo air transport demand, the capacity of Jeju International Airport has been approaching its limit. Even though in COVID-19 crisis which has started from Nov 2019, Jeju International Airport still suffers from strong demand in terms of air passenger and cargo transportation. However, it is an undeniable fact that the delay has also increased in Jeju International Airport. In this study, we analyze the correlation between weather and delayed departure operation based on both datum collected from the historical airline operation information and aviation weather statistics of Jeju International Airport. Adopting machine learning techniques, we then analyze weather condition Jeju International Airport and construct a delay prediction model. The model presented in this study is expected to play a useful role to predict aircraft departure delay and contribute to enhance aircraft operation efficiency and punctuality in the Jeju International Airport.

• 한국작물학회지
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• 제27권4호
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• pp.291-295
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• 1982

The biosphere of the earth is not only about to overpass the limit to meet the food demand of the world but also the stability of its food production has been also jeopardized by the disasters and pests, especially by the unpredictable weather disasters. In addition the agricultural and industrial pollution against biosphere aggravates the unstability of agricultural production and constitutes a threat in securing the food of the world. In Korea the yield level of crops has been greatly enhanced by the improved agrotechnologies and varietal improvement, but the yield variability due to unfavorable weather events and pests remained unchanged with the change in time. Among weather-related disasters the drought and flood damages has occurred most frequently and impacted most greatly on the agricultural production and its stability. During last decade (1970-l980) the rice production experienced the average annual loss of 0.544 million metric ton which was composed of 0.21 million M/T by climatic disaster, 0.21 million M/T by disease and 0.12 million M/T by insects, and the annual loss of upland crop production from climatic disasters amounted to 0.06 million metric tons. Especially in 1980, the global climatic disasters due to cold or hot temperature endangered the agricultural production all over the world and also the rice production of Korea recorded the unprecedented yield reduction of about 30 percent due to cool summer weather. Nowadays, the unusual weather conditions are prevaling throughout the world, and agro-meteologists predict that the unpredictable cool summer and drought will often attack the rice and other crops in 1980's. To meet the coming weather unstability and to secure the stable crop production, multilateral efforts should be rendered. Therefore, the Korea Society of Crop Science, which commemorates the 20th anniversary of its founding, prepared the symposium on Meteological Stress in Crop Production and its Countermeasures to discuss the decrease in agricultural production due to weather-related disasters and to devise the multilateral counter-measures against the unfavorable weather events.

• 설비공학논문집
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• 제28권12호
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• pp.467-476
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• 2016

As the energy saving issues become one of the important global agenda, the building simulation method is generally used to predict the inside energy usage to establish the power-saving strategies. To foretell an accurate energy usage of a building, proper and typical weather data are needed. For this reason, typical weather data are fundamental in building energy simulations and among the meteorological factors, the solar irradiation is the most important element. Therefore, preparing solar irradiation is a basic factor. However, there are few places where the horizontal solar radiation in domestic weather stations can be measured, so the prediction of the solar radiation is needed to arrive at typical weather data. In this paper, four solar radiation prediction models were analyzed in terms of their applicability for domestic weather conditions. A total of 12 regions were analyzed to compare the differences of solar irradiation between measurements and the prediction results. The applicability of the solar irradiation prediction model for a certain region was determined by the comparisons. The results were that the Zhang and Huang model showed the highest accuracy (Rad 0.87~0.80) in most of the analyzed regions. The Kasten model which utilizes a simple regression equation exhibited the second-highest accuracy. The Angstrom-Prescott model is easily used, also by employing a plain regression equation Lastly, the Winslow model which is known for predicting global horizontal solar irradiation at any climate regions uses a daily integration equation and showed a low accuracy regarding the domestic climate conditions in Korea.

• 한국태양에너지학회 논문집
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• 제29권5호
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• pp.81-87
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• 2009

Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design.. Building designers often now predict the performance of buildings simulation programmes that require hourly weather data. However, not all weather stations provide hourly data. Climate prediction models such as HadCM3 also provide the daily average dry bulb temperature as well as the maximum and minimum. Hourly temperature values are available for building thermal simulations that accounts for future changes to climate. In order to make full use of these predicted future weather data in building simulation programmes, algorithms for downscaling daily values to hourly values are required. This paper describes a more accurate method for generating hourly temperature values in the South Korea that uses all three temperature parameters from climate model. All methods were evaluated for accuracy and stability in terms of coefficient of determination and cumulative error. They were compared with hourly data collected in Seoul and Ulsan, South Korea.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.528-531
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• 2021

Nowadays recommendation systems are so ubiquitous, where our many decisions are being done by the means of them. We can see recommendation systems in all areas of our daily life. Therefore the research of this sphere is still so active. So far many research papers were published for clothing recommendations as well. In this paper, we propose the clothing-recommendation system according to user emotion and weather information. We used social media to analyze users' 6 basic emotions according to Paul Eckman theory and match the colour of clothing. Moreover, getting weather information using visualcrossing.com API to predict the kind of clothing. For sentiment analysis, we used Emotion Lexicon that was created by using Mechanical Turk. And matching the emotion and colour was done by applying Hayashi's Quantification Method III.

• 디지털융복합연구
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• 제17권4호
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• pp.249-254
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• 2019

기상이변 및 미세먼지 현상이 대두되면서 발생 가능한 문제의 대안으로 사용자들은 모바일 날씨 어플리케이션을 찾는다. 그러나 발전된 기술을 기반으로 정보를 제공하는 어플리케이션이 있음에도 불구하고 예측오류 및 날씨로 인해 발생되고 있는 환경, 경제 등의 문제들은 기대한 만큼 줄어들지 않고 있다. 따라서 본 연구의 목적은 사용자가 모바일 어플리케이션을 통해 변덕스러운 날씨에 대비를 철저히 할 수 있는 요소를 모바일로부터 찾는 것이다. 연구방법으로는 UX 전문가를 중심으로 닐슨의 휴리스틱 사용성 평가를 진행하여 '원기날씨'와 '케이웨더'사례를 평가 분석한 뒤 취약한 점을 도출했다. 분석 결과 제품은 다양한 기능과 제공하는 정보를 제공하고 있지만 사용자에게 접근성이 낮았으며, '정보전달'에 주 초점을 두고 있어 딱딱한 느낌주고 있었다. 본 연구는 향후 날씨 앱 실증연구시 사용자를 움직이는 적용방법의 효용성에 대한 단서를 얻는데 중요한 기초가 될 수 있으며, 모바일 어플리케이션 사용성 결과를 행동경제학 이론이라는 신선한 접근으로 분석했다는 점에서 의의가 있다.

• 한국환경과학회지
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• 제27권7호
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.