• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.59-64
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• 2010

Building a firebreak against surface forest fire is a typical indirect suppression method that stops spread of flame by removing surface fuel, such as fallen leaves and bushes. In the sense of fire dynamic, building a firebreak is to set a section which will block thermal energy from igniting on virgin fuel. This study suggests and evaluates a calculation method for width of firebreak against surface fire for variant wind and slope conditions by applying the Point Source Model (PSM) to fallen leaves of Pinus densiflora. Width of firebreak was measured based on the distance the threshold radiant heat igniting Pinus densiflora fallen leaves at the heat flux of $4.9\;kW/m^2$ reaches. As a result, at the wind velocity of 0~5 m/s and on the slope of $0{\sim}50^{\circ}$, the appropriate width of a firebreak was 0.35~0.65 m for the mean flame height and 0.75~1.05 m for the maximum flame height. Accordingly, considering the factor of safety, the most appropriate width of a firebreak is 1.05 m based on the maximum flame height. Additional comparative analyses through experiments and field surveys are deemed necessary to determine appropriate widths of firebreak for different types of surface fuel.

• Fashion & Textile Research Journal
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• v.13 no.4
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• pp.624-634
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• 2011

The purpose of this study is to develop the pattern of safety clothes used at flat or mountainous areas and to identify the pattern of safety clothes by conducting experimental evaluation of virtual wear. Three subjects were selected, based on fire fighters' physical constitution. A prototype design for safety clothing was determined after in-depth interviewing of professionals and surveying of Forest service staff and related agency. Wearing test should be carried out in the order of pattern making, virtual and real wearing evaluation. For data analysis, technical statistical values should be obtained by using body measurements of subject, frequency analysis and T-test. The jacket is designed to have a front extension and the entire length of clothing enough for wearer to put on it over ordinary shirts or sweater. The collar of jacket is of round type. Cyber reality enables to identify the movement and activity of virtual fitting model and to find out errors or problems in safety clothing prior to on-the-spot wear test, thus raising the precision level of pattern. There was significant difference between real and virtual fit preference. The results show that the virtual try-on system need the development of a specific style.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.305-317
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• 2022

To investigate the blossom infection risk of fire blight on pears, the program Maryblyt has been executed from 2018 to 2022 based on meteorological data from central-Korean cities where fire blight has occurred as well as from southern Korean cities where the disease has not yet occurred. In the past five years, years with the highest risk of pear blossom blight were 2022 and 2019. To identify the optimal time for spraying, we studied the spray mode according to the Maryblyt model and recommend spraying streptomycin on the day after a "High" warning and then one day before forecasted precipitation during the blossom period. Maryblyt also recommends to initiate surgical controls from mid-May for canker blight symptoms on pear trees owing to over-wintering canker in Korea. Web-cam pictures from pear orchards at Cheonan, Icheon, Sangju, and Naju during the flowering period of pear trees were used for comparing real data and constructing a phenological model. The actual starting dates of flowering at southern cities such as Sangju and Naju were consistently earlier than those calculated by the model. It is thus necessary to improve the forecasting model to include field risks by recording the actual flowering period and the first day of the fire blight symptoms, according to the farmers, as well as mist or dew-fall, which are not easily identifiable from meteorological records.

• 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.

• Entomological Research
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• v.48 no.6
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• pp.505-513
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• 2018

The red imported fire ant is considered one of the most notorious invasive species because of its adverse impact on both humans and ecosystems. Public concern regarding red imported fire ants has been increasing, as they have been found seven times in South Korea. Even if red imported fire ants are not yet colonized in South Korea, a proper quarantine plan is necessary to prevent their widespread distribution. As a basis for quarantine planning, we modeled the potential distribution of the red imported fire ant under current climate conditions using six different species distribution models (SDMs) and then selected the random forest (RF) model for modeling the potential distribution under climate change. We acquired occurrence data from the Global Biodiversity Information Facility (GBIF) and bioclimatic data from WorldClim. We modeled at the global scale to project the potential distribution under the current climate and then applied models at the local scale to project the potential distribution of the red imported fire ant under climate change. Modeled results successfully represent the current distribution of red imported fire ants. The potential distribution area for red imported fire ants increased to include major harbors and airports in South Korea under the climate change scenario (RCP 8.5). Thus, we are able to provide a potential distribution of red imported fire ant that is necessary to establish a proper quarantine plan for their management to minimize adverse impacts of climate change.

• Fire Science and Engineering
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• v.24 no.6
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• pp.1-6
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• 2010

A smoldering ground fire can be a probable cause of reignition of surface fire when transmitted from Fermentation layer to Humus layer with temperature higher than that of ignition. Purpose of this paper is to identify experimental methodology on the potential risk of a smoldering ground fire, which is similar to the real surface fuel bed, and its combustion characteristics. The fuel model designed in this study is composed of 3 layers such as Litter layer, Fermentation layer and Humus layer and 8 Thermocouples are set through 3 layer at each boundary and in between to detect the temperature change and duration of smoldering and propagation velocity. As a result, it was observed that ignition conditions in the boundary between L layer and F layer determined transmission and non-transmisstion to F-H layer. In addition, range of critical humidity at which a smoldering ground fire was transmitted in a material layer was 33~44% and when temperature exceeds $350^{\circ}C$, likelihood of transmission of a smoldering ground fire was high. In the research, the experimental model for multi-layer smoldering ground fire is suggested and information about propagation of smoldering fire, possibility of reignition according to moisture content, propagation velocity and temperature change are obtained, Also, the built-up methods were established to help analyze basic characteristics of smoldering ground fire.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.11-16
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• 2003

Soil loss by the rains has effect on natural environment. But It is difficult to find out the data that is surveyed in watershed. In this paper, we choose USLE erosion model, which could be connected easily with GSIS and available generally, and extracted factors which is entered model by using GSIS spatial analysis method. Especially, As revised USLE model, It should be applied in watershed and as it calculated soil loss before Idlest fire and behind, it analysed the degree that it have an effect on soil loss. As each analyzed factors and the result of soil loss estimate consist of 22m-pixel size, we could identify soil loss by each pixel and distribution pattern.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.843-847
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• 2023

In this paper, we propose a model for detecting efficient fire identification to prevent fires that can lead to various industrial accidents, farmland and large forest fires, with the widespread use of various chemicals and flammable substances as modern technology advances. This paper presents an algorithm that can detect fire smoke in a high-efficiency and short time using images, and an algorithm based on SVM(Support Vector Machine) and K fold cross-verification technologies. By analyzing images, fire and smoke detection algorithms have relatively superior detection performance compared to existing algorithms, and the analysis of fire and smoke characteristics detected in this paper is analyzed stably and efficiently and is expected to be used in various fields that may be exposed to fire risks in the future.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.39-55
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• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.