• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.235-245
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• 2023

Forest fuel moisture content is a crucial factor influencing the combustion rate and fuel consumption during forest fires, significantly impacting the occurrence and spread of wildfires. In this study, meteorological data were gathered using a meteorological measuring device (HOBO data logger) installed in the south and north slopes of Kangwon National University Forest, as well as on bare land outside the forest, from November 1, 2021, to October 31, 2022. The objective was to analyze the relationship between meteorological data and fuel moisture content. Fuel moisture content from the ground cover on the south and north slopes was collected. Fallen leaves on the ground were utilized, with a focus on broad-leaved trees (Prunus serrulata, Quercus dentata, Quercus mongolica, and Castanea crenata) and coniferous trees (Pinus densiflora and Pinus koraiensis), categorized by species. Additionally, correlation analysis with fuel moisture content was conducted using temperature (average, maximum, and minimum), humidity (average, minimum), illuminance (average, maximum, and minimum), and wind speed (average, maximum, and minimum) data collected by meteorological measuring devices in the study area. The results indicated a significant correlation between meteorological factors such as temperature, humidity, illuminance, and wind speed, and the moisture content of fuels. Notably, exceptions were observed for the moisture content of the on the north slope and that of the ground cover of Prunus serrulata and Castanea crenata.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• Fire Science and Engineering
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• v.26 no.1
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• pp.49-60
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• 2012

The change in fuel moisture in accordance with the number of days after rainfall is an important factor in predicting forest fire dangers and supporting forest fire rangers. Therefore, in order to clear up these forest fire occurrence conditions, forest fire danger levels for surface fuel 0.6 cm or lower, 0.6~3.0 cm, 3.0~6.0 cm, and 6.0 cm or above by fallen leaves layer, humus layer, soil layer, and diameter after rainfall of 5.0 mm and higher in accordance with tree density in 2008, 2009 Spring/Autumn Young Dong region have been analyzed. Research showed an approximate 17 % fuel moisture which is a dangerous forest fire occurrence level after 5 days from rainfall in medium-density areas and 3 days after rainfall in loose-density areas of Spring time in the fallen leaves layer. On the other hand, the humus layer showed a 40 % or higher fuel humidity even after 6 days from rainfall regardless of the season, while the upper and lower parts of the soil layer had a little change. In loose-density areas with 0.6 cm or less surface fuel per diameter in Spring time, the fuel humidity displayed a dangerous level in fire forest occurrence after 3 days, and 4days in medium-density areas, and for loose-density areas with 0.6~3.0 cm surface fuel per diameter in Autumn time it showed a dangerous level in forest fire occurrence after 3 days, and for medium-density areas, 5 days. In the case of 3.0~6.0 cm of fuel moisture per diameter in both Spring and Autumn times, even after 6 days, low and medium-density areas showed that they maintain fuel moisture and therefore the dangers of forest fires were very low, and in the case of 6.0 cm or higher, it showed 25 % or higher fuel moisture even after 6 days from rainfall regardless of the season.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.305-310
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• 1997

There are many parameters in prediction of forest fire spread. The variables such as fuel moisture, fuel loading, wind velocity, wind direction, relative humidity, slope, and solar aspect have important effects on fire. Particularly, wind and slope factors are considered to be the most important parameters in propagation of forest fire. Generally, slope effect cause different wind distribution in mountain area. However, this effect is disregarded in complex geometry. In this paper, wind is estimated by applying computational fluid dynamics to the forest geometry. Wind velocity data is obtained by using CFD code with Newtonian model and slope is calculated with geometrical data. These data are applied fer 2-dimentional forest fire spreading algorithm with Korean ROS(Rate Of Spread). Finally, the comparison between the simulation and the real forest fire is made. The algorithm spread of forest fire will help fire fighter to get the basic data far fire suppression and the prediction to behavior of forest fire.

• Journal of the Korean Wood Science and Technology
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• v.48 no.6
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• pp.917-935
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• 2020

In this study, an automated sensor to measure forest fire surface fuel moistures was developed to predict changes in the moisture content and risk of forest fire surface fuel, which was indicators of forest fire occurrence and spread risk. This measurement sensor was a method of automatically calculating the moisture content of forest fire surface fuel by electric resistance. The proxy of forest fire surface fuel used in this sensor is pine (50 cm long, 1.5 cm in diameter), and the relationship between moisture content and electrical resistance, R(R:Electrical resistance)=2E(E:Exponent of 10)+13X(X:Moisture content)-9.705(R²=0.947) was developed. In addition, using this, the software and case of the automated measurement sensor for forest fire surface fuel moisture were designed to produce a prototype, and the suitability (R²=0.824) was confirmed by performing field monitoring verification in the forest. The results of this study would contribute to develop technologies that can predict the occurrence, spread and intensity of forest fires, and are expected to be used as basic data for advanced forest fire risk forecasting technologies.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.434-437
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• 2008

In this study, we developed a fuel moisture variation prediction model on each day after precipitation during a spring forest fire exhibition period. For this research, we selected plots in pine forest on Sam-Chuck si and Dong-hae si in Kangwon do according to a forest density(low, mediate, high) and classified a surface woody fuel by a diameter.(below 0.6cm, $0.6{\sim}3cm$, $3{\sim}6cm$, and above 6cm). A validity of this model was verified by applying a fuel moisture variation after precipitation in this spring. In the result, $R^2$ was $0.76{\sim}0.92$. This model will be a useful for improvement of a forest fire danger rate forcast through a prediction a fule moisture in forest.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.29-35
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• 2011

A forest fire was one of the huge disasters and damaged human lifes and a properties. Therefore, many countries operated forest fire forecasting systems which developed from forest fire records, weather data, fuel models and etc. And many countries also estimated future state of forest fire using a long-term climate forecasting like GCMs and prepared resources for future huge disasters. In this study, we analyzed relationships between forest fire occurrence and meteorological factors (the minimum temperature ($^{\circ}C$), the relative humidity (%), the precipitation (mm), the duration of sunshine (hour) and etc.) for developing a estimating tools, which could forecast forest fire regime under future climate change condition. Results showed that forest fires in this area were mainly occurred when the maximum temperature was $10{\sim}200^{\circ}C$, when the relative humidity was 40~60%, and when the average wind speed was under 2m/s. And forest fires mainly occurred at 2~3 day after rainfall.

• 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.

• Journal of the Korean Wood Science and Technology
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• v.39 no.6
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• pp.512-520
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• 2011

This study was preformed to investigate the characteristics of the green drying system for utilizing heat wasted during carbonization process. The green drying system utilizing waste heat is one of environment-friendly equipments because it needs no other energies from fossil fuel and etc. In this study, waste heat from three kilns was collected by stainless connection pipe, and in the green drying system the temperature and humidity was hardly changed. Charcoal charecteristics as fixed carbon, refining degree, hardness, pH, calorific value, and charcoal yield were analyzed to investigate kiln performance due to installation of green drying system. As a result, the green dry system installation hardly affected the characteristics of charcoal. In conclusion, the green drying system can be applied to maximize the profit of the farm household income and contribute to reduce fossil energy.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.