• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.4
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• pp.235-239
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• 2005

An accurate fire danger rating model can contribute to effective forest fire prevention activities. This study evaluates the national forest fire danger rating index based on forest fire statistics data from 1999 to 2002. The number of fires was related to the forest fire danger rating index $(R^2=0.67)$, and no correlation was found with burned areas. A one-way ANOVA test between forest fire danger rating levels and forest fire statistics data indicated that a difference in the number of fires was found among 'danger', 'precaution' and 'none' levels, but 'precaution' and 'none' levels could not be delineated. In the case of a burned area, no difference was found among the three levels.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.643-648
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• 2006

It is important to understand the patterns of forest fire in terms of effective prevention and suppression activities. In this study, the monthly forest fire occurrences and their burned areas were investigated to enhance the understanding of the patterns of forest fire in Korea. The statistics of forest fires in Korea, 1970 through 2005, built by Korea Forest Service was analyzed by using time series analysis technique to fit ARIMA models proposed by Box-Jenkins. The monthly differences in forest fire characteristics were clearly distinguished, with 59% of total forest fire occurrences and 72% of total burned area being in March and April. ARIMA(1, 0, 1) was the best fitted model to both the fire accurrences and the burned area time series. The fire time series have a strong relation to the fire occurrences and the burned area of 1 month and 12 months before.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.301-306
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• 2020

In this study, analyzed national forest fire statistics by cause, year, region, and damage scale based on the National Fire Data System. as a result, the main cause of forest fires was the most frequent human error. forest fires occurred in areas with high population density. and it was confirmed that the Widest area of forest damage was Gang-Won province, which is rich in forestry resources. by season, it occurred a lot in spring because of the warm temperature and strong wind and low humidity. such disasters directly damage forest facilities such as house and cultural properties as well as destruction of natural resources. therefore in this study, made a suggestion plan for prevention from forest fire with forest fire prevention comprehensive plan of MFOA.

• Journal of Forest and Environmental Science
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• v.38 no.4
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• pp.219-228
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• 2022

This study examined the relationship between the number of forest fires and days with no rainfall based on the national forest fire statistics data of the Korea Forest Service and meteorological data from the Open MET Data Portal of the Korea Meteorological Administration (KMA; data.kma.go.kr) for the last 30 years (1991-2021). As for the trend in precipitation amount and non-rainfall days, the rainfall and the days with rainfall decreased in 2010 compared to those in 1990s. In terms of the number of forest fires that occurred in February-May accounted for 75% of the total number of forest fires, followed by 29% in April and 25% in March. In 2000s, the total number of forest fires was 5,226, indicating the highest forest fire activity. To analyze the relationship between regional distribution of non-rainfall periods (days) and number of forest fires, the non-rainfall period was categorized into five groups (0 days, 1-10 days, 11-20 days, 21-30 days, and 31 days or longer). During the spring fire danger season, the number of forest fires was the largest when the non-rainfall period was 11-20 days; during the autumn fire precaution period, the number of forest fires was the largest when the non-rainfall period was 1-10 days, 11-20 days, and 21-30 days, showing differences in the duration of forest fire occurrence by region. The 30-year trend indicated that large forest fires occurred only between February and May, and in terms of the relationship with the non-rainfall period groups, large fires occurred when the non-rainfall period was 1-10 days. This signifies that in spring season, the dry period continued throughout the country, indicating that even a short duration of consecutive non-rainfall days poses a high risk of large forest fires.

• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.54-63
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• 2006

The number of forest fire under various conditions such as year, month, time, day of the week, region, damaged species, cause, and damaged area are checked, and the statistics of the forest fire causing materials in recent 14 years ('91-'04) are analyzed. The result shows that the year majority of forest fires had happened in last 14 year was 2001 and most of forest fire occurred in April, Sunday, around 14:00 to 15:00. The most damaged region is Gyeongsangbuk-Do, followed by Gangwon-Do, Jeollabuk-Do, and Gyeonggi-Do. The most damaged species is pine tree. The main causes of forest fires are accidental fire and incineration of a field boundary; however, recently, incendiarism is increased. The result of analysis on the damaged area shows that small fires under 5 ha occurred most frequently and large fires (over 30 ha) occurred mostly in Kangwon province (44.2%). The result also shows that the large forest fires (1,113 minutes) require 7.5 time more than the small forest fires (148 minutes). Especially, since average damaged area caused by large forest fire was about 470 ha per incident.

• Journal of Korean Society of Forest Science
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• v.97 no.1
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• pp.1-9
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• 2008

It is necessary to examine relationship between spatial patterns of forest types and characteristics of forest fires for efficient management of fire and forest. By the ecoregions of South Korea, we computed landscape indices for whole types of forests(landscape level) and pine forests(class level), and analyzed characteristics of forest fires using statistics of forest fires from 1991 to 2006. We performed canonical correlation analysis to model the relationship between the landscape indices and the statistics of forest fires. At landscape level, forest patches were larger and more complex in the ecoregions which had higher percentage of forest area. At class level, pine forest patches were more complex and closer to neighbor patches in the coastal ecoregions. The ecoregions including metropolitan areas and cities had more frequent fire occurrences per 1,000ha, while mountainous coastal ecoregions had more burned areas and faster spread of fire growth rate. The canonical correlation between the landscape indices for pine forests and the statistics of forest fires was statistically significant at the 0.05 level and explained more than 70% of the variation in fire variables. The results showed that combustion time per fire was longer in the ecoregions which had larger and more aggregated pine forest patches.

• Fire Science and Engineering
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• v.23 no.1
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• pp.15-20
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• 2009

For the effective management of forest fire, understanding of regional forest fire patterns is needed. In this paper, forest fire ignition and spread characteristics were analyzed based on forest fire statistics. Fire occurrences, burned area, rate of spread, and burned area per fire between 1991 and 2007 were parameterized for the cluster analysis, which results were displayed using GIS to detect spatial patterns of forest fire. Administrative districts such as cities and counties were classified into 5 clusters by fire susceptibility. Metropolitan areas had fire characteristics that were infrequent, slow rate of spread, and small burned area. However, 4 cities and counties showing fast rate of spread, and large burned area, in the eastern regions of Taeback Mountain range, were the most susceptible areas to forest fire. The next vulnerable cities and counties were located in the West and South Coast area.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.457-469
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• 2022

Purpose: The purpose of this study is to present the direction of improvement of safety management by reviewing the current status of forest fire accidents that are becoming larger throughout the year and the problems of the response system. Method: Domestic and foreign literature survey and statistics of recent forest fire accidents by Statistics Korea investigated and analyzed the cause, number of damage, and suggested ways to improve forest fire safety management through domestic and foreign forest fire response systems. Result: Through the analysis of the causes of recent wildfires and overseas response cases, measures to improve the safety management of wildfires in terms of hardware, software, and humanware were derived. Conclusion: The plan to improve forest fire safety management was classified into three main categories and presented, and it should be embodied through further related research.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.