Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Development of the National Integrated Daily Weather Index (DWI) Model to Calculate Forest Fire Danger Rating in the Spring and Fall

봄철과 가을철의 기상에 의한 전국 통합 산불발생확률 모형 개발

  • Won, Myoungsoo (Division of Forest Ecology and Climate Change, National Institute of Forest Science) ;
  • Jang, Keunchang (Division of Forest Ecology and Climate Change, National Institute of Forest Science) ;
  • Yoon, Sukhee (Division of Forest Ecology and Climate Change, National Institute of Forest Science)
  • 원명수 (국립산림과학원 기후변화생태연구과) ;
  • 장근창 (국립산림과학원 기후변화생태연구과) ;
  • 윤석희 (국립산림과학원 기후변화생태연구과)
  • Received : 2018.02.28
  • Accepted : 2018.12.19
  • Published : 2018.12.30
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Abstract

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.

본 연구는 현 국가산불위험예보시스템에서 실시간으로 분석되는 기상에 의한 산불발생확률 모형의 문제점을 개선하기 위하여 수행하였다. 기존 시스템의 문제점은 제주도를 포함한 9개의 도별 산불발생확률모형으로 인해 행정경계 지역에서 산불위험등급(관심-주의-경계-심각 4단계)의 차이가 발생하여 산불담당자들간 혼선을 야기할 수 있고, 이로 인해 인접 시군 경계 간 산불대응력이 떨어질 수 있다는 것이다. 이의 해결을 위해 기존 9개의 산불발생확률모형을 하나로 통합하는 산불발생확률모형을 개발하여 신뢰도 검증과 실제로 산불이 발생한 지점에서 예측된 산불위험지수 값을 추출하여 정확도 평가를 실시하였다. 새롭게 개발한 기상에 의한 봄철과 가을철의 전국 통합 산불발생확률 모형(DWI)은 국립산림과학원에서 운영하는 국가산불위험예보시스템에 반영하여 예측모델을 개선하였다. 연구 결과, 봄철 산불발생에 영향을 주는 기상변수로는 해당 시간대의 평균기온, 상대습도, 실효습도, 평균풍속이었으며, 가을철은 평균기온, 상대습도, 평균풍속으로 나타났으며 모두 99% 신뢰수준에서 통계적으로 유의한 것으로 나타났다. 봄철과 가을철의 전국 통합 산불발생확률 모형은 각각 $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$으로 표본내 예측력은 봄철이 71.7%, 가을철은 86.9%로 나타나 모형의 적합도는 매우 높은 것으로 나타났다. 기존의 도별 9개 모형을 하나의 전국 통합 모형으로 적용할 경우 인접 행정경계에서 발생하는 위험등급의 차이를 해소하여 산불조심기간 중 발효되는 산불위험 단계별 조치사항의 이행에 혼란을 피할 수 있다는 장점이 있다. 새롭게 개발한 전국 통합 산불발생확률 모형(DWI)의 예측 결과 검증을 위해 2014년 봄철 발생한 산불 66건을 대상으로 산불위험지수의 정확도를 평가하였으며, 주의 단계인 산불위험지수 51이상으로 예측된 지역에서 실제로 산불이 발생한 비율은 기존 9개 모형에서 74.24% (산불 49건), 새롭게 개발한 전국 통합 모형에서는 83.33% (산불 55건)가 발생하여 약 9%의 정확도 향상을 보였다. 개발된 모형은 현재 운영중인 산림청 국립산림과학원의 국가산불위험예보시스템에 반영하여 산불이 가장 많이 발생하는 봄철과 가을철 건조시기의 산불발생위험을 정확히 예측하여 산불예방은 물론 진화자원의 효율적인 배치를 통해 시간과 인적 경제적 비용을 절감하고 산불피해를 최소화 할 수 있는 선택과 집중의 산불정책에 일조할 수 있을 것으로 기대된다.

Keywords

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Fig. 1. DWI frequency of the forest fire occurrence and non-fire of the national integrated model by spring weather.

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Fig. 2. DWI frequency of the forest fire occurrence and non-fire of the national integrated model by fall weather.

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Fig 3. The result of forest fire danger rating by 9 daily weather index (DWI). Forest fire danger rating index multiply DWI by 10.

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Fig. 5. The comparison of forest fire danger rating between nine models (left) and one national integrated model (right) in South Korea (Sample on the 13:30 April 6, 2014).

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Fig 4. The result of forest fire danger rating by national integrated daily weather index (DWI). Forest fire danger rating index multiply DWI by 10.

Table 1. The statistics of forest fire occurrences during the spring season (January to June) and the fall season (September to December) from 2000 to 2010

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Table 2. The estimation of national integrated daily weather index (DWI) model by logistic regression analysis

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Table 3. National integrated daily weather index (DWI) model to calculate the forest fire danger rating by Spring and Fall (Tmean : average temperature, Eh : effective humidity, Rh : relative humidity, Wmean : average wind speed)

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Table 4. Verifying confidence of the national integrated daily weather index(DWI) model by spring and fall

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Table 5. The probability interval of the national integrated daily weather index(DWI) model by spring and fall

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Table 6. The result of forest fire danger rating index between nine models and one national integrated model at the 66 forest fire ignition points

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