• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.165-172
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• 2015

The extreme weather conditions become frequent and severe with global warming. To prevent and cope forest disaster like a forest fire, we need an accurate micrometeorological prediction system for mountainous regions. This study addressed the forest fires occurred at Bonghwa and Gangneung in March, 2013. We constructed and optimized the prediction system that were required to interpret and simulate the forest micrometeorology. At first, we examined WRF physical sensitivity. Subsequently, KMA AWS observation data were assimilated using three-dimensional variation data assimilation method. The effectiveness of the assimilation was examined by using AWS observations enhanced with the Forest Research Institute observations. Finally, The 100 meters spatial resolution wind data were obtained by using the MUKLIMO for the given wind vector from WRF.

• 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 the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.126-130
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• 2015

• The Journal of Bigdata
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• v.6 no.1
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• pp.133-144
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• 2021

This paper derives the fire risk of buildings in Seoul through the prediction of property damage and the occurrence of fires. This study differs from prior research in that it utilizes variables that include not only a building's characteristics but also its affiliated administrative area as well as the accessibility of nearby fire-fighting facilities. We use Ensemble Voting techniques to merge different machine learning algorithms to predict property damage and fire occurrence, and to extract feature importance to produce fire risk. Fire risk prediction was made on 300 buildings in Seoul utilizing the established model, and it has been derived that with buildings at Level 1 for fire risks, there were a high number of households occupying the building, and the buildings had many factors that could contribute to increasing the size of the fire, including the lack of nearby fire-fighting facilities as well as the far location of the 119 Safety Center. On the other hand, in the case of Level 5 buildings, the number of buildings and businesses is large, but the 119 Safety Center in charge are located closest to the building, which can properly respond to fire.

• Smart Media Journal
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• v.12 no.3
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• pp.77-84
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• 2023

Forest fires are representative natural disasters resulting from dramatic global climate change in these modern times. When forest formation is insufficient due to forest damage caused by fire, secondary damages such as landslides occur during the winter thawing period and heavy rains. In most countries, only a limited area is managed as CCTV-centered monitoring systems for forest management. For the landslide prediction, markers containing 3D spatial coordinates were located on the slopes of the danger areas in advance. Then 3D mapping and angle of repose were obtained by periodic drone imaging. The recognition range and angle of view of markers were defined, and a new method for predicting signs of landslides in advance was presented in this study.

• Fire Science and Engineering
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• v.28 no.6
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• pp.13-21
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• 2014

Forest fire has a number of variables and since the effects of wind fields are bigger than any other variables, it is essential to know wind direction and velocity for the forest fire extinguishing techniques and the prediction of fire spread. With regards to the local area that has a high chance of forest fire, the data from meteorological observatory in the area is used for the estimation of wind velocity. It is relatively easy to obtain automatic weather station (AWS) data which are available for the whole nation. There is a chance that the data from the weather station may be different with the actual data at the mountain areas. In this study simply shaped hills (Sae-byeol hill of Jeju Island and port Ma-geum in An-myeon Island in the sea side) were selected as the experimental locations to minimize the distortion of the wind field by the adjacent geographic features. Spot measurements and analysis of computational fluid dynamics (CFD) for the given geographic features were conducted to examine and compare their consistency. As a conclusion It is possible to predict wind patterns in these simple locations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.199-200
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• 2012

전 세계적으로 산불로 인한 산림 자원의 손실로 인한 피해는 막대하다. 산불로 인한 인명 및 재산 피해는 증가하는 추세이다. 또한 산불로 인한 산림 자원의 손실은 생태계에 회복되기 힘든 상처를 남긴다. 이런 산불을 분석하고 예방하기 위해 다양한 연구가 진행되고 있으나, 산불의 발생을 예측 할 수 있는 연구는 부족한 실정이다. 본 논문은 미래 예측 연구에 많이 사용되는 마코프 체인을 이용하여 산불을 예측 할 수 있는 산불 예측 모델링을 제안 하고 그 기대 효과에 대해 논의한다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).