• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.70-78
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• 2011

In this study, we develop a fire stochastic simulation model by season based on the historical fire data in Korea. The model is utilized to generate sequences of fire events that are consistent with Korean fire history. We employ a three-stage approach. First, a random draw from a Bernoulli distribution is used to determine if any fire occurs for each day of a simulated fire season. Second, if a fire does occur, a random draw from a geometric multiplicity distribution determines their number. Last, ignition times for each fire are randomly drawn from a Poisson distribution. This specific distributional forms are chosen after analysis of Korean historical fire data. Maximum Likelihood Estimation (MLE) is used to estimate the primary parameters of the stochastic models. Fire sequences generated with the model appear to follow historical patterns with respect to diurnal distribution and total number of fires per year. We expect that the results of this study will assist a fire manager for planning fire suppression policies and suppression resource allocations.

• 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.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.173-174
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• 2010

산불감시활동에 의한 탐지확률을 높이고, 감시자원의 효율적인 이용을 위해서는 산불 감시 우선지역에 대한 분석이 요구된다. 따라서 산불감시 우선지역을 추출하기 위해 가시권 분석과 산불발생확률 분석을 실시하였으며, 중첩을 통해 가중치를 부여하였다. 가시권 분석은 탐지확률과 관련된 감시자원의 높이, 산불연기높이, 지형의 roughness에 따른 유효가시거리 인자를 다르게 하여 실시하였다. 산불발생확률은 로지스틱 회귀분석모형과 연료, 기상, 지형인자 및 토지피복, 접근성 인자 DB를 이용하여 분석하였다. 개발된 산불감시 우선지역 분석체계는 산불감시자원의 효율성 제고를 위한 기초자료로 활용될 수 있을 것으로 예상되었다.

• Fire Science and Engineering
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• v.19 no.3 s.59
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• pp.64-69
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• 2005

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is developing the forest fire occurrence probability model by means of forest site characteristics such as soil type, topography, soil texture, slope, and drainage and forest fire sites. Conditional probability analysis and GIS were used in developing the forest fire occurrence probability model that was used in the classification of forest fire occurrence risk regions.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.37-46
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is to classify hazard regions where forest fires occur based on the factors that contribute to the occurrence of forest fires. Forest fire sites in the Uiseong-gun, Gyeongsangbuk-do were surveyed according to the factors of forest type and topographic characteristics where the forest fires occurred. We used a correlation analysis to determine the forest fire occurrence factors and a conditional probability analysis and GIS to determine a forest fire danger index. The resulting forest fire danger index was used in the classification of forest fire occurrence risk regions.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.57-64
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, the forest fire danger rating system was developed to estimate forest fire risk by means of weather, topography, and forest type. Forest fires occurrence prediction needs to improve continually. Logistic regression and spatial analysis was used in developing the forest fire occurrence probability model. The forest fire danger index in accordance to the probability of forest fire occurrence was used in the classification of forest fire occurrence risk regions.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.1-6
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• 2012

To achieve the forest fire management goals such as early detection and quick suppression, fire resources should be allocated at high probability area where forest fires occur. The objective of this study was to develop and validate models to estimate spatially distributed probabilities of occurrence of forest fire. The models were builded by exploring relationships between fire ignition location and forest, terrain and anthropogenic factors using logistic regression. Distance to forest, cemetery, fire history, forest type, elevation, slope were chosen as the significant factors to the model. The model constructed had a good fit and classification accuracy of the model was 63%. This model and map can support the allocation optimization of forest fire resources and increase effectiveness in fire prevention and planning.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.126-135
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• 2011

In this study, the algorithm for priority area of forest fire surveillance was developed to enhance the effectiveness of fire detection. The high priority surveillance area for forest fire detection was defined as the area with not only low value of viewshed analysis of the lookouts and detection cameras but also high fire occurrence probability. To build the priority map, fuzzy function and map algebra were used. The analysis results of Bonghwa-gun, Gyeongbuk Province, showed that the surveillance priority of central and southern area is higher than north area. This algorithm could be used in the allocation of fire prevention resources and selection of suitable point for new fire detection system.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.414-414
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• 2023

지구온난화로 인한 기후변화로 산불 발생이 증가하고 있는 추세이다. 이로 인해 무강우기간의 증가, 기온의 상승, 습도 감소 등의 문제가 발생하고 있다. 이중 기온의 상승과 습도의 감소는 산불과 큰 연관성이 있다. 따뜻하고 건조한 날씨는 토양수분의 감소로 이어지며, 이는 곧 식물의 활성도의 감소로 이어진다. 봄철 식물의 활성도가 감소하게 되면 자연스레 산불 발생 위험도가 높아지기 마련이다. 이러한 현상은 국내에서도 관찰 가능하다. 따뜻하고 건조한 국내 겨울철 날씨는 봄철 토양수분의 감소와 직결되며, 건조해진 토양수분으로 인해 봄철인 3월~5월 산불 발생 확률이 증가하게 된다. 실제로 최근 10년간 국내에서 산불이 많이 발생한 계절은 봄철이며, 산불로 인한 피해 면적 및 피해 금액이 가장 큰 기간도 봄철이다. 따라서 봄철 산불에 대한 각별한 주의가 요구 되어지고 있다. 본 연구에서는 국내 유역을 대상으로 산불 위험도 지수에 대한 평가를 진행하고자 한다. 이에 PRMS 모형을 이용해 봄철 토양수분을 모의하여 산불 위험도 지수와 토양수분의 상관성을 나타내고자 한다. 또한, 산불 발생 건수를 활용하여 산불 위험도 지수에 대한 적절성을 평가하고자 한다.