• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.101.1-101.1
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• 2009

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.04a
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• pp.448-453
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• 2010

본 연구에서는 캐나다 연료 수분지수의 정확도를 보정하여, 산불발생의 예측 효율성을 높이기 위해, 기존 입력 값인 상대습도와 실효습도(Effective humidity)를 이용한 결과를 상호 비교 분석하였다. 캐나다 연료수분지수인 미세연료수분지수, 가뭄지수, 부식층수분지수의 결과를 비교한 결과 이에 따라 실효습도를 사용한 캐나다 기상지수의 민감도가 상대습도를 사용하여 산출된 지수보다 민감도가 떨어지는 것으로 조사되었다. 또한, 산불 발생 예측의 지표인 미세연료 수분 지수의 분석결과, 실효습도를 이용하여 산출된 미세연료 수분지수의 적중률이 떨어지는 것으로 조사되었다. 따라서, 미세연료 수분지수의 적용보다 상대습도의 적용이 효과적인 것으로 분석되었다.

• 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.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.10a
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• pp.370-374
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• 2010

기후변화는 우리생활에서 많은 영향을 줄 것으로 예측되고 있다. 산불 또한, 발생 빈도와 강도 면에서 상당한 영향을 받을 것으로 예측된다. 본 연구에서는 기후변화모형(GCM)과 캐나다 산불 기상 지수의 미세연료 수분지수를 활용하여, 우리나라에서 기후변화 후 예측 되는 산불 발생의 가능성과 산불 계절의 변화를 예측하여, 향후 산불 방제 정책의 기본 자료로 삼고자 하였다. 밸런스형 사회가 유지될 경우의 미세 연료 수분 지수의 분석 결과, 산불 계절이 현재 보다 변화하는 것으로 나타나 이에 대한 사전 대비가 필요한 것으로 분석되었다.

• 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 Cadastre & Land InformatiX
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• v.47 no.2
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• pp.107-120
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• 2017

There are many wildfire risk indices worldwide, but objective comparisons between such various wildfire risk indices and surface dryness indices have not been conducted for the wildfire cases in Korea. This paper describes a sensitivity analysis on the wildfire risk indices and surface dryness indices for Korea using LDAPS(Local Analysis and Prediction System) meteorological dataset on a 1.5-km grid and MODIS(Moderate-resolution Imaging Spectroradiometer) satellite images on a 1-km grid. We analyzed the meteorology-based wildfire risk indices such as the Australian FFDI(forest fire danger index), the Canadian FFMC(fine fuel moisture code), the American HI(Haines index), and the academically presented MNI(modified Nesterov index). Also we examined the satellite-based surface dryness indices such as NDDI(normalized difference drought index) and TVDI(temperature vegetation dryness index). As a result of the comparisons between the six indices regarding 120 wildfire cases with the area damaged over 1ha during the period between January 2013 and May 2017, we found that the FFDI and FFMC showed a good predictability for most wildfire cases but the MNI and TVDI were not suitable for Korea. The NDDI can be used as a proxy parameter for wildfire risk because its average CDF(cumulative distribution function) scores were stably high irrespective of fire size. The indices tested in this paper should be carefully chosen and used in an integrated way so that they can contribute to wildfire forecasting in Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.116-130
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• 2019

In South Korea, forest fire occurrences are increasing in size and duration due to various factors such as the increase in fuel materials and frequent drying conditions in forests. Therefore, it is necessary to minimize the damage caused by forest fires by appropriately providing the probability of forest fire risk. The purpose of this study is to improve the Daily Weather Index(DWI) provided by the current forest fire forecasting system in South Korea. A new Fire Risk Index(FRI) is proposed in this study, which is provided in a 5km grid through the synergistic use of numerical weather forecast data, satellite-based drought indices, and forest fire-prone areas. The FRI is calculated based on the product of the Fine Fuel Moisture Code(FFMC) optimized for Korea, an integrated drought index, and spatio-temporal weighting approaches. In order to improve the temporal accuracy of forest fire risk, monthly weights were applied based on the forest fire occurrences by month. Similarly, spatial weights were applied using the forest fire density information to improve the spatial accuracy of forest fire risk. In the time series analysis of the number of monthly forest fires and the FRI, the relationship between the two were well simulated. In addition, it was possible to provide more spatially detailed information on forest fire risk when using FRI in the 5km grid than DWI based on administrative units. The research findings from this study can help make appropriate decisions before and after forest fire occurrences.