• Proceedings of KOSOMES biannual meeting
• /
• 2018.06a
• /
• pp.78-78
• /
• 2018

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.6D
• /
• pp.685-691
• /
• 2009

The purpose of this paper is to classify the traffic condition in freeway by the impacts of road weather. The factor analysis showed that weather factors, which are considered as influential, are identified as weather condition (rain or clear), temperature and sight distance with RWIS and VDS data in Seohae bridge used. The result of ANOVA shows that weather is dividedinto clear and rainy; temperature into below and equal or above $5^{\circ}C$ and sight distance into below or equal or above 10km. Based on those factors, the freeway traffic condition has been classified as five different types. The flow-speed model for each traffic conditions was proposed, which was not significant due to the lack of smaple data. Although not sufficient, the methodology to categorize traffic situation model presented in this paper may shed light on the idea for the future and can be used for proper traffic management for each weather condition.

• Journal of Global Scholars of Marketing Science
• /
• v.12
• /
• pp.121-134
• /
• 2003

Weather marketing is firms' effort to incorporate changes of diverse weather factors into marketing planning and activities. The concept has already been applied in many products with mostly seasonal variation. However researches in this area have been limited only in practical areas and has not been supported by scientifIc approaches. Here, we investigated the effect of diverse weather factors like temperature, rain and wind on product sales based on empirical data and scientifIc methodology. For this, we selected the fashion clothing items in department stores. We tried to fInd the relationship between daily sales of clothing items and daily whether factors. Results showed that there is a meaningful relation between the two factors.

• Journal of Environmental Science International
• /
• v.25 no.6
• /
• pp.839-851
• /
• 2016

The effect of weather on disease was investigated based on results reported in academic papers. Weather-sensitive disease was selected by analyzing the frequency distributions of diseases and correlations between diseases and meteorological factors (e.g., temperature, humidity, pressure, and wind speed). Correlations between disease and meteorological factors were most frequently reported for myocardial infarction (MI) (28%) followed by chronic ischemic heart disease (CHR) (12%), stroke (STR) (10%), and angina pectoris (ANG) (5%). These four diseases had significant correlations with temperature (meaningful correlation for MI and negative correlations for CHR, STR, and ANG). Selecting MI, as a representative weather-sensitive disease, and summarizing the quantitative correlations with meteorological factors revealed that, daily hospital admissions for MI increased approximately 1.7%-2.2% with each $1^{\circ}C$ decrease in physiologically equivalent temperature. On the days when MI occurred in three or more patients larger daily temperature ranges ($2.3^{\circ}C$ increase) were reported compared with the days when MI occurred in fewer than three patients. In addition, variations in pressure (10 mbar, 1016 mbar standard) and relative humidity (10%) contributed to an 11%-12% increase in deaths from MI and an approximately 10% increase in the incidence of MI, respectively.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2004.11a
• /
• pp.394-397
• /
• 2004

Working-day calculation with weather factors of construction-site has estimated wethout proper data. They usually estimate it with their own experience and intuition. It causes not only economic loss to time-adjustment but also conflict with each participants. Moreover, weather estimation becomes worse than before, due to tendency of recently weather change. So, in this paper we present optimal estimation method as assessment by period of the arithmetical mean methods. For that, we analyse characteristic of the regions and weather change of temperature and rainfall which affects time.

• Atmosphere
• /
• v.31 no.1
• /
• pp.73-83
• /
• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Proceedings of the KIEE Conference
• /
• 2004.11b
• /
• pp.60-62
• /
• 2004

Transmission line outage is influenced by several weather factors: wind, rain snow, temperature, cloud and humidity. And most power system reliability studies assume a failure rate. It can be calculated by transmission line outage data and weather data. Also weather is divided into normal weather and adverse weather by failure rate analysis. The effect of failure rate is discussed with both normal weather and adverse weather. It can be used in effective information about system operation and planing.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.391-393
• /
• 2004

Transmission line outage is influenced by several weather factors: wind, rain snow, temperature, cloud and humidity. So, in this paper try to see how much each weather factors have effect on the transmission line outage and it is analyzed that which weather variables have close relation with transmission line historical outage data in KEPCO systems. These statistic correlation analysis may provide system operators useful information about system operation and planing.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.20 no.11
• /
• pp.752-759
• /
• 2008

This paper is to make up the climograph using standard weather data of the region of Seoul. It is made up by existed standard weather data of which the subjected region is Seoul in order to easily use work. The factors of weather data are outside air temperature and its absolute humidity, total solar radiation, amount of clouds, wind direction, and wind velocity. The standard weather data are verified by comparing with values of the existed degree day method. As the result of their verification, the difference of the data showed less than 3% each other. And, reliability of standard weather data is thought to be same as those of degree day.

• Journal of Fisheries and Marine Sciences Education
• /
• v.23 no.3
• /
• pp.351-360
• /
• 2011

In order to reduce the casualties of fishing boats, the author analyzed the fishing boat accident on the 412 cases in Korean maritime safety tribunal for the 2005~2009, and then studied the relation between the weather element and the accidents. According to this studies, the occurring ratio of sea casualty for fishing boat in fog weather was appeared 1 boat per 1.6 days. It means that the restricted visibility condition gives the most influence on the fishing boat accident. The casualties in winter season from November to next January occurred 139(33.7%), and small boats less than 50tons broke out more casualties with 68.4%. From this we can find that small fishing boats are very deeply affected on the sea weather condition. According to the boat types for fishing the casualty of jig boat was ranked first, and collision accident account for first with 77.9% for the types of casualties. As mentioned above, most sea casualties for small fishing boats were resulted from the human factors such as poor watch keeping in invisibility and the bad sea condition, it is necessary for navigation operators and the manager to take more attention to the meteorological factors.