• The Korean Journal of Ecology
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• v.26 no.4
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• pp.199-202
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• 2003

According to the energy hypothesis, the energy input per unit area primarily determines species richness in regions of roughly equal area. Some energy-related ecological research included identification of major climatic variables to determine regional species richness. In this study, the local butterfly species richness was examined to find out whether weather variables affected the local distribution or abundance of butterfly populations. Butterfly monitoring data from May 2001 to April 2002 taken at Mt. Yudal, Mokpo, in the southwestern part of Korea, and six weather variables (monthly mean values of temperature, precipitation, evaporation, wind speed, air pressure, and sunlight) were analyzed. Multiple regression analysis showed that only temperature explained 80% and 70% of the variability of log-transformed number of species and individuals, respectively, indicating that temperature played an important role in local species richness. Furthermore, global warming could affect the abundance and distribution of butterflies regionally as well as locally.

• Journal of the military operations research society of Korea
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• v.23 no.1
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• pp.76-87
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• 1997

This paper deals with reliability and MTTF analysis of a non-repairable man-machine system operating under different weather conditions. The system consists of a hardware(machine) and a two-operator standby subsystem such as the air combat maneuvering of fighters with dual seat. The failure times for the subsystems follow the exponential distribution with constant parameter. By considering not only the effect on hardware component but also the weather conditions and human performance factors such as the operator's errors, a Markov model is presented as a method for evaluating the system reliability of time continuous operation tasks. Laplace transforms of the various state probabilities have been derived and then reliability of the system, at any time t, has been computed by inversion process. MTTF has also been computed.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.187-189
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• 2004

This paper investigates the effect of weather factors(such as winds, rain, snows, temperature, clouds and humidity) on transmission line outages. The result shows that weather variables have significant effects on the transmission line historical outages and the relationship between them is nonlinear. Multiple regression analysis using Logit model is proved to be appropriate in forecasting line failure rate in KEPCO systems. It could also provide system operators with useful informations about system operation and planing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Navigation and Port Research
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• v.30 no.3 s.109
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• pp.227-233
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• 2006

As North-east Chinese parts including Shanghai port grow rapidly, competition among the Far-East parts to be a hub port is getting higher, somebody has even raised a crisis of Busan port. However there are same constraints an the North-east parts such as weather aggravation and long distance from main truck routes. When we consider the competitiveness of port, weather aggravation should be considered as one of the significant factors. But previous studies have rarely examined these kinds of external factors of port operation Therefore, this study analyze constraints an the North-east Chinese parts through a survey of same national flag shipping companies and agencies of foreign shipping companies. The result shows that the mast significant constraint in the North-east ports is weather aggravation which causes problems for regular schedules of shipping, operation cast and customer service qualities etc.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.313-326
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• 2023

Owing to adverse weather conditions, there is a heightened focus on actively researching the regulation of the sowing date in field crop cultivation. Soybean, a prominent field crop with extensive acreage and production, is a photophilic and thermophilic crop characterized by short-day photoperiodism. Identifying the optimal sowing time is crucial for mitigating the effects of severe weather conditions on soybean yield. Precise control over the timing of soybean sowing is the key to minimizing yield reduction due to unfavorable weather conditions. Temperature, photoperiod, and their interplay are the most significant factors influencing soybean cultivation among various weather factors. We conducted an experiment using three Korean soybean cultivars with varied maturities (Hwangkeumol: early maturing and Daewonkong and Pungsannamulkong: late maturing) in 2013 and 2014. Our investigation covered aspects of soybean growth, development, yield components, isoflavones, and visual seed quality. Across all three varieties, isoflavone levels increased with later sowing dates, while other measured components exhibited significant variations based on the sowing date. This study also provides valuable insights for the selection of suitable cultivars that perform well in soybean cultivation at various durations of maturity.

• 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.

• 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.