• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.1
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• pp.82-90
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• 2012

Four different buildings having various wall construction are analyzed for the effect of wall mass on the thermal performance and inside building air and wall temperature transient and also for calculating the energy consumption load. This analytical study was motivated by the experimental work of Burch et al. An analytical solution of one-dimensional, linear, partial differential equations is obtained using the Laplace transform method, Bromwich and modified Bromwich contour method. A simple dynamic model using steady state analysis as simplified methods is developed and results of energy consumption loads are compared with results obtained using the analytical solution. Typical Meteorological Year data are processed to yield hourly average monthly values. This study is conducted using weather data from two different locations in Korea: Daegu having severe weather in summer and winter and Jeju having mild weather almost all year round. There is a significant wall mass effect on the thermal performance of a building in mild weather condition. Buildings of heavyweight construction with insulation show the highest comfort level in mild weather condition. A proportional controller provides the higher comfort level in comparison with buildings using on-off controller. The steady state analysis gives an accurate estimate of energy load for all types of construction. Finally, it appears that both mass and wall insulation are important factors in the thermal performance of buildings, but their relative merits should be decided in each building by a strict analysis of the building layout, weather conditions and site condition.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.157-163
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• 2010

It is well known that weather conditions are closely related with the number and severity of traffic accidents. At present, installation of safety countermeasures including systems is common approach to reduce the damage of traffic accidents at expressways. In this study, the differences of causation factors to influence traffic accidents considering road alignment characteristics and weather conditions. In order to identify the relationship between road and weather conditions, discriminant analysis has been performed with 500 traffic accident data at expressways. Weather conditions are divided into several categories such as snow, sunny, rain, fog, and cloud. Also, road conditions such as types of pavements, grades are analyzed. As the results, major impacting road conditions to traffic accidents are concrete pavement and 3% or more down grades. In these road conditions, visible distance will be reduced and actual braking distances will be increased. This study shows that the expressway sections under concrete pavement and down grades should be more cautious than other sections. It also shows that fog condition is the mose dangerous situation in terms of traffic accidents.

• Solar Energy
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• v.18 no.3
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• pp.31-39
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• 1998

Selection of a right weather data set has been considered as one of important factors for a successful building energy audit process. A set of 30 year raw weather data base for six major cities has been developed to provide the weather data file for building energy audit and retrofit analysis in Korea. The program named as KWDP(KIER Weather Data Processor) uses the DB to produce a right data set for a specific building energy performance simulation program like DOE2.1E. A program called WMAKE has been developed to generate the right set of input parameters for DOE2.1E weather utility program. The set of the programs could provide the right weather data for specific building energy audit and retrofit analysis.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.957-966
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• 2018

This study intend to induce citizen's voluntary preliminary disaster prevention activity to reduce damage of typhoon that occurs every year. For this purpose, a survey was conducted to develop Typhoon information contents. The number of samples used in the survey was set to 500 people, and citizens living in Jeju, Busan, and Jeonlanam-do were surveyed for areas with high typhoon disasters in order to develop practical and efficient information. The survey consisted of perception about natural disaster, how to get and use weather information, satisfaction with typhoon information and requirements. The general public perceived the typhoon as the first natural disaster. As a result of responding to the method of obtaining and utilizing weather information, the frequency of collecting weather information at the time of issuance of typhoon special report is higher than usual. The purpose of using weather information is clear and the response rate is high for the purpose of disaster prevention. The medium mainly collecting weather information is Internet portal site and mobile phone besides television. The current satisfaction with typhoon weather information is 34.8%, in addition to the accuracy of prediction, it is necessary to improve the information (that is content) provided. Specific responses to the content were investigated not only for single meteorological factors, but also for possible damage and potential countermeasures in the event of a disaster such as a typhoon. As can be seen from the above results, people are requested to provide information that can be used to detect and cope with disasters. The development of new content using easy accessible media will contribute to the reduction of damages caused by the typhoon that will occur in the future, and also to the disaster prevention activity.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.230-236
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• 2024

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

• Korean Journal of Agricultural Science
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• v.11 no.2
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• pp.270-277
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• 1984

The effects of factors of natural air drying were evaluated by the simulation model for rough rice drying. The factors were airflow rate, harvest date, initial moisture content and weather conditions. For simulation, the RICEDRY (Chang et al., 1983) was used. Then, the applicability of the model and the feasibility of rough rice drying by natural air were tested under the tropical weather conditions of Costa Rica.

• Journal of Construction Engineering and Project Management
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• v.5 no.2
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• pp.1-10
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• 2015

There is a high confidence based on scientific evidence that climate is changing over time. Now climate change is considered as one of the challenges facing the construction industry. As no project is risk free and climate change has a strong impact on the different phases of the construction project lifecycle. This research aimed at providing a platform of knowledge for the construction management practitioners about the impacts of climate change on the construction projects lifecycle, identify the most dangerous climate change factors on the construction project lifecycle, and identify the most affected phase by climate change factors through the construction projects lifecycle. The study depended on the opinions of civil engineers who have worked in the construction projects field among the reality of Gaza Strip. Questionnaire tool was adopted as the main research methodology in order to achieve the desired objectives. The questionnaire included 127 factors in order to obtain responses from 88 construction practitioners out of 98 representing 89.79% response rate about the influence of climate change on the generic lifecycle of construction projects. The results deduced that the most significant influence on the construction project lifecycle was related to the extreme weather events, rainfall change, and temperature change respectively. There was a general agreement between the respondents that the most affected phase by temperature, rainfall, and extreme weather events is the execution phase. The results also asserted with a high responses scale on the need to alternative procedures and clear strategies in order to face the climate change within construction industry.

• Safety and Health at Work
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• v.9 no.1
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• pp.42-52
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• 2018

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.