• Convergence Security Journal
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• v.22 no.2
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• pp.99-104
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• 2022

This study provided a prediction model for solar energy production in Yeongam province, Jeollanam-do. The model was derived from the correlation between climate changes and solar power production in Yeongam province, Jeollanam-do, and presented a prediction of solar power generation through the regression analysis of 6 parameters related to weather and solar power generation. The data used in this study were the weather and photovoltaic production data from January in 2016 to December in 2019 provided by public data. Based on the data, the machine learning technique was used to analyzed the correlation between weather change and solar energy production and derived to the prediction model. The model showed that the photovoltaic production can be categorized by the three-stage production index and will be used as an important barometer in the agriculture activity and the use of photovoltaic electricity.

• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.73-85
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• 1999

This study was conducted to reveal the characteristics of the early growth by locality for Korean white pine planted in Gapyung and Kwangju, Kyunggi-Do and Youngdong, Choongchungbuk-Do. The effects of local climatic conditions as one of environmental factors on the growth were also analyzed. For this, several stand variables such as number of trees survived, mean DBH, mean height, basal area per hectare, and volume per hectare by stand age were measured and summarized for each locality. Based on these statistics, periodic annual increments for 8 years from stand age 10 to 18 were calculated for each of stand variables. A topoclimatological technique, for the estimation of local climatic conditions, which makes use of empirical relationships between the topography and the weather in study areas was applied to produce reasonable estimates of monthly mean, maximum, minimum temperatures, relative humidity, precipitation, and hours of sunshine over remote land area where routine observations are rare. From these monthly estimates, 17 weather variables such as warmth index, coldness index, index of aridity etc. which affect the tree growth, were computed for each locality. The periodic annual increments were then correlated with and regressed on the weather variables to examine effects of local weather conditions on the growth. Gapyung area provided the best conditions for the growth of Korean white pine in the early stage and Kwangju area ranked second. On the other hand, the growth pattern in Youngdong ranked last overall as expected. It is also found that the local growth patterns of Korean white pine in juvenile stage were affected by typical weather conditions. The conditions such as low temperature, high relative humidity, and large amount of precipitation provide favorable environment for the growth of Korean white pine. Especially, the diameter growth, basal area growth, and volume growth are mainly influenced by the amount of precipitation. However, it is proved that the height growth is affected by both the precipitation and temperature.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.348-356
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• 2018

Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behavior and its spread. Thus, meteorological factors as well as topographical and forest factors were considered in the fire danger rating systems. This study aims to develop an advanced national integrated daily weather index(DWI) using weather data in the spring and fall to support forest fire prevention strategy in South Korea. DWI represents the meteorological characteristics, such as humidity (relative and effective), temperature and wind speed, and we integrated nine logistic regression models of the past into one national model. One national integrated model of the spring and fall is respectively $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$ and all weather variables significantly (p<0.01) affected the probability of forest fire occurrence in the overall regions. The accuracy of the model in the spring and fall is respectively 71.7% and 86.9%. One integrated national model showed 10% higher accuracy than nine logistic regression models when it is applied weather data with 66 random sampling in forest fire event days. These findings would be necessary for the policy makers in the Republic of Korea for the prevention of forest fires.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2447-2453
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• 2013

In this study, the effects of weather conditions such as rainfall, discomfort index, snowfall, and sensible temperature on public transport demand in Seoul were analyzed using statistical data. The reasons were also derived from the survey. The data for the analysis were collected over the weekdays and weekends, and seasonal data of summer and winter were also gathered separately. Rainfall amount, discomfort index, and sensible temperature except snowfall amount, whose samples were insufficient, decreased the public transport demand by 2-7%. Rainfall amount and sensible temperature were statistically significant. Correlation analysis also showed that rainfall amount and sensible temperature are highly correlated with the demand. To find the reasons, the survey was conducted on citizens living in the Seoul Metropolitan Area. About 30% of the respondents wished to give up using bus when rainfall was heavy or temperature was low. On the contrary, auto and subway users increased by 10%. The results of this study could be used as the basic data when the public transportation planning or operation related policies according to the weather condition are concerned.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.95-100
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• 2009

Fine fuel moisture code (FFMC), a main component of forest fire weather index(FWI) in the Canadian forest fire danger rating system(CFFDRS), indicated a probability of ignition through expecting a dryness of fine fuels. According to this code, a rising of temperature and wind velocity, a decreasing of precipitation and decline of humidity in a weather condition showed a rising of a danger rate for the forest fire. In this study, we analyzed a weather condition during 5 years in Kangwon province, calculated a FFMC and examined an application of FFMC. Very low humidity and little precipitation was a characteristic during spring and fall fire season in Kangwon province. 75% of forest fires during 5 years occurred in this season and especially 90% of forest fire during fire season occurred in spring. For developing of the prediction model for a forest fire occurrence probability, we used a logistic regression function with forest fire occurrence data and classified mean FFMC during 10 days. Accuracy of a developed model was 63.6%. To improve this model, we need to deal with more meteorological data during overall seasons and to associate a meteorological condition with a forest fire occurrence with more research results.

• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.

• Journal of Climate Change Research
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• v.3 no.2
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• pp.89-100
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• 2012

This study analyzes spatio-temporal variability of discomfort index for summer the during the past ten years(2001~2010) in the Korean Peninsula, and considers the application possibility of discomfort index as a preliminary data for various phenomenon of society based on the analysis. Discomfort index defined as daily representative value was estimated using hourly temperature and humidity data which are observed 60 weather stations managed by Korea Meteorological Administration. The result indicates that the discomfort index in summer keeps the level at which one feels unpleasant, and the level increased steadily as temperature is rising. And discomfort index in 3 pm and on August are the highest during the day and year. Gangwon-do have shown the lowest discomfort index among the provinces. Variability analysis of discomfort index due to climate changes can be used for making policies in various fields such as industry and public health field.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.897-904
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• 2016

We investigated the local climatological characteristics of the mountain adjacent the Dongyeong herb garden in Chilgok. We established one set of automatic weather system (AWS) on a hill where development of herb garden is in progress. The observations were continued for 2 years(2013. 07-2015.06). In this study, we analyzed the observed data comparing the data of Gumi meteorological observatory (GMO). The results showed that the air temperature(relative humidity) of Dongyeong herb garden were lower(higher) than those of GMO. Especially the differences are more during warm climate season. It means that the gaps of thermal environment between two points are mainly caused by the evaporation effects of forest. In addition, we analyzed the warmth indices(warmth index and coldness index) with the observed air temperature. The warmth and coldness indices indicate about 107 and -12, respectively. The values correspond to warm temperature climate.

• Journal of Biosystems Engineering
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• v.25 no.3
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• pp.195-202
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• 2000

This study was conducted to recognize a possibility that cone index can be used as a means of evaluating the tillage workability. Cone indexes were measured every 24 hours after rainfall at the experimental plots, and the rotary and plowing operations were conducted at the same time. The workability was evaluated on a basis of three categories of good, fair and poor depending on the quality of the performed works. Although the workability was affected by many factors such as soil type, moisture content ground slope and weather condition, the duration and amount of rainfall were of most influence. Results of the study showed that a good workability was resulted from the cone indexes greater than an average of 552 kPa for rotary operations and 671 kPa for plowing operations. Fair work was obtained with cone indexes greater than an average of 331 kPa for rotary operations and 459 kPa for plowing operations. The cone indexes less than an average of 171 kPa and 149 kPa resulted in poor workabilities for rotary and plowing operations, respectively. The experimental results may provide a general guideline for evaluating the tillage workability by cone index.

• Journal of Environmental Science International
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• v.28 no.2
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• pp.277-285
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• 2019

Heat watch warning systems are operating in Korea and several other countries (China, the United Kingdom, France, the United States, Germany, Australia, and Japan). The heat wave indices used in this system are the heat index, perceived temperature, the wet bulb globe temperature, and the daily maximum temperature. To improve the heat wave advisory and warning system, some suggestions have been made. The meteorological-health index (i.e., indirect index), has especially been proposed in previous studies. This information should be provided not only to vulnerable groups (seniors, infants, and children), but also to outdoor workers who may be particularly exposed to heat waves. In addition, to have sufficient preemptive response times, the need for an extension of the heat watch warning period was suggested. Finally, the subdivision of administrative units and risk stages was proposed.