• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.11a
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• pp.213-217
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• 2009

Weather factors affect cost increases and progress management under construction. Because progress schedule is delayed by weather factors, the construction costs are increased. It is an essential element to control the progress schedule applying weather factors to the progress management. This study applies monthly working-day percentages which is estimated by databases of past weather information to RTPM system. Through do progress management in construction projects exactly, will try to minimize risk of process control that do that is to weather factors. Also, will compare calamity in safety supervision side that do that is to weather factors beforehand. Based on the factors and the expected impact of factors together with the weather data during the last 50 years in Seoul region gathered from Korea. Through it, calculated number of month working day of RCA's structural steel work. Studied way that apply to RTPM system.

• Journal of Environmental Science International
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• v.12 no.4
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• pp.399-417
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• 2003

The seasonal variation and frequency of rainfalls of Korea peninsula in Changma period show strong local weather phenomenon because of it's topographical and geographical factors in Northeast side of Asia. Based on weather entropy(statistical parameter)-the amount of average weather information-and information ratio, we can define each area's weather representativeness, which can show us more constant form included topographical and geographical factors and seasonal variation. The data used for this study are the daily precipitation and cloudiness during the recent ten years(1990-1999) at the 73 stations in Korea. To synthesize weather Entropy, information ratio of decaying tendency and half$.$decay distance, Seoul's weather representativeness has the smallest in Summer Changma period. And Puyo has the largest value in September.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.71-78
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• 2016

This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.219-224
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• 2022

The Korea Meteorological Administration(KMA) has been released life-related indexes such as 'Life industrial weather information' and 'Safety weather information' while other countries' meteorological administrations have been made 'Human-biometeorology' and 'Health meteorology' indexes that concern human sensibility effections to diverse criteria. Although human sensibility changes have been studied in psychological research criteria with diverse and innumerous application areas, there are not enough studies that make data mining based validation of sensibility change factors. In this research I made models to estimate sensibility change caused by weather factors such as temperature and humidity, and validated by collecting sensibility data from SNS text crawling and weather data from KMA public dataset. By Logistic Regression, I clarify factors affecting sensibility changes.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.397-402
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• 2013

In super-tall building construction projects, schedule risk factors which vertically change and are not found in the low and middle-rise building construction influence duration of a project by vertical attribute; and it makes hard to estimate activity or overall duration of a construction project. However, the existing duration estimating methods, that are based on quantity and productivity assuming activities of the same work item have the same risk and duration regardless of operation space, are not able to consider the schedule risk factors which change by the altitude of operation space. Therefore, in order to advance accuracy of duration estimation of super-tall building projects, the degree of changes of these risk factors according to altitude should be analyzed and incorporated into a duration estimating method. This research proposes a simulation model using Monte Carlo method for estimating activity duration incorporating schedule risk factors by weather conditions in a super-tall building. The research process is as follows. Firstly, the schedule risk factors in super-tall building are identified through literature and expert reviews, and occurrence of non-working days at high altitude by weather condition is identified as one of the critical schedule risk factors. Secondly, a calculating method of the vertical distributions of the weather factors such as temperature and wind speed is analyzed through literature reviews. Then, a probability distribution of the weather factors is developed using the weather database of the past decade. Thirdly, a simulation model and algorithms for estimating non-working days and duration of each activity is developed using Monte-Carlo method. Finally, sensitivity analysis and a case study are carried out for the validation of the proposed model.

• Journal of Forest and Environmental Science
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• v.34 no.3
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• pp.249-252
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• 2018

Climate is one of most important environmental factors on the forest ecosystem. This study was conducted to analyze the characteristics of meteorological factors in the forest area and weather stations from July 2015 to June 2016 in Cheuncheon and Hongcheon of Kangwon Province in Korea. The HOBO data logger was installed for meteorological analysis in forests area (site 1 and site 2). The meteorological data from the HOBO data logger compared with meteorological data of the weather station. The meteorological data used for the analysis was monthly mean temperature ($^{\circ}C$), monthly mean minimum temperature ($^{\circ}C$), monthly mean maximum average temperature ($^{\circ}C$), and monthly mean relative humidity (%). As a result of this study, the mean temperature ($^{\circ}C$) of forest area was relatively lower than weather station which is the outside the forest area, and the mean maximum temperature ($^{\circ}C$) of weather station was relatively higher than that of forest area. The mean relative humidity (%) was higher in forest area than weather station.

• Journal of Information Technology Applications and Management
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• v.24 no.3
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• pp.93-105
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• 2017

Demands of cosmetics are affected not only by the well-known elements such as brand, price, and customer's consumption capacity, but also by some latent factors, for example, weather and air environment. Due to complexity and dynamic changes of the above factors, their influences can hardly be estimated in an accurate way by the traditional approaches such as survey and questionnaires. Through modeling and statistical analysis of big data, this article studied the impacts of weather condition and air quality on customer flow and sales of the cosmetics distributors in China, and found several hidden influencing factors. It provided a big-data based method for the analysis of unconventional factors on cosmetics marketing in the changing weather condition and air environment.

• Fire Science and Engineering
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• v.29 no.5
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• pp.88-95
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• 2015

We propose a weather-related service for fire risk assessment in order to increase fire safety awareness in everyday life. The proposed service offers a fire risk assessment level according to weather forecasts and a degree of fire risk according to fire factors under certain weather conditions. In order to estimate the fire risk, we produced a risk matrix through data mining with a decision tree using investigation data and weather data. Through the proposed service, residents can calculate the degree of fire risk under certain weather conditions using the fire factors around them. In addition, they can choose from various solutions to reduce fire risk. In order to demonstrate the feasibility of the proposed services, we developed a system that offers the services. Whenever weather forecasting is carried out by the Korea Meteorological Administration, the system produces the fire risk assessment levels for seven major cities and nine provinces of South Korea in an online process, as well as the fire risk according to fire factors for the weather conditions in each region.

• Agribusiness and Information Management
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• v.6 no.1
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• pp.1-11
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• 2014

The purpose of this study is to develop the growth prediction model that can predict growth and development information influencing on the production of citrus fruits. The growth model was developed to predict the floral leaf ratio, number of fruit sets, fruit width, and overweight fruits depending on the main period of growth and development by considering the weather factors because the fruit production is influenced by weather depending on the growth and development period. To predict the outdoor-grown citrus fruit production, the investigation result for the standard farms is used as the basic data; in this study, we also understood that the influence of weather factors on the citrus fruit production based on the data from 2004 to 2013 of the outdoor-grown citrus fruit observation report in which the standard farms were targeted by the Agricultural Research Service and suggested the growth and development information prediction model with the weather information as an independent variable to build the observation model. The growth and development model for outdoor-grown citrus fruits was assumed by using the Ordinary Least Square method (OLS), and the developed growth prediction model can make a prediction in advance with the weather factors prior to the observation investigation for the citrus fruit production. To predict the growth and development information of the production of citrus fruits having a great ripple effect as a representative crop in Jeju agriculture, the prediction result regarding the production applying the weather factors depending on growth and development period could be applied usefully.

• Journal of the Korean Institute of Landscape Architecture
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• v.21 no.4
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• pp.69-82
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• 1994

The purpose of this study is to estimate the probable period of the planting work in consideration of weather factors. The impact degree of weather factors on the control of planting schedule was measured by the possible working days on the basis of weather condition. To establish the weather standard, the researcher analyzed the questionnaires on the manager of planting work and also the meteorological data for 10 years(1983-1992) in Seoul. The results are as follows; $\circled1$ The possible period of the planting work is from March 17 to May 18 Spring and from September 26 to December 15 in Autumn during a year. $\circled2$ The problem working days of the planting work(106-130) days per year) are less than the building construction days(174 days per year), because of handling the living material of plants, specially in summer and winter.