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

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.781-791
• /
• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Geomechanics and Engineering
• /
• v.13 no.5
• /
• pp.861-879
• /
• 2017

Coal and gas outburst is a serious dynamic disaster that occurs during coal mining and threatens the lives of coal miners. Currently, coal and gas outburst is commonly predicted using single indicator and its critical value. However, single indicator is unable to fully reflect all of the factors impacting outburst risk and has poor prediction accuracy. Therefore, a more accurate prediction method is necessary. In this work, we first analyzed on-site impacting factors and precursors of coal and gas outburst; then, we constructed a Fisher discriminant analysis (FDA) index system using the gas adsorption index of drilling cutting ${\Delta}h_2$, the drilling cutting weight S, the initial velocity of gas emission from borehole q, the thickness of soft coal h, and the maximum ratio of post-blasting gas emission peak to pre-blasting gas emission $B_{max}$; finally, we studied an FDA-based multiple indicators discriminant model of coal and gas outburst, and applied the discriminant model to predict coal and gas outburst. The results showed that the discriminant model has 100% prediction accuracy, even when some conventional indexes are lower than the warning criteria. The FDA method has a broad application prospects in coal and gas outburst prediction.

• Journal of Ship and Ocean Technology
• /
• v.4 no.4
• /
• pp.19-33
• /
• 2000

For the prevention of marine accidents based on human factor, the risk assessment analysis procedure is proposed which consists of (1) the structural analysis of marine accident, (2) the estimation of incidence probability based on the Fault Tree analysis, (3) the prediction of ef-fectiveness to reduced the accident risk by suitable countermeasures in the specified functional system, and (4) the risk assessment by means of minimizing of the total cost expectation and the background risk. As a practical example, the risk assessment analysis for preventing is investigated using the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.351-361
• /
• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2017.05a
• /
• pp.46-47
• /
• 2017

Korea rapidly arranged urbanization and overpopulation with high growth of economy and all kinds of decrepit facilities are scattered all over the downtown. If there is a strong wind in fire, fire is rapidly increased by various fire spread factors. And Korea cannot build prediction model of urban fire combustion phenomena because there is no studies that physically explains the suitable flame phenomena for its real state. In this study, based on the Japanese Urban fire simulation to target the traditional market area and suitability of fire risk assessment were reviewed.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2013.11a
• /
• pp.226-227
• /
• 2013

Korea rapidly arranged urbanization and overpopulation with high growth of economy and all kinds of decrepit facilities are scattered all over the downtown. If there is a strong wind in fire, fire is rapidly increased by various fire spread factors. And Korea cannot build prediction model of urban fire combustion phenomena because there is no studies that physically explains the suitable flame phenomena for its real state. In this study, based on the Japanese Urban fire simulation to target the building congested Area and suitability of fire risk assessment were reviewed.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2013.05a
• /
• pp.87-88
• /
• 2013

Korea rapidly arranged urbanization and overpopulation with high growth of economy and all kinds of decrepit facilities are scattered all over the downtown. If there is a strong wind in fire, fire is rapidly increased by various fire spread factors. And Korea cannot build prediction model of urban fire combustion phenomena because there is no studies that physically explains the suitable flame phenomena for its real state. In this study, based on the Japanese Urban fire simulation to target the wooden building congested Area and suitability of fire risk assessment were reviewed.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_3
• /
• pp.1847-1859
• /
• 2022

Wildfires in North Korea can have a directly or indirectly affect South Korea if they go south to the Demilitarized Zone. Therefore, this study calculates the regional optimized Forest Fire Danger Index (FFDI) based on Local Data Assessment and Prediction System (LDAPS) weather data to obtain forest fire risk in North Korea, and applied it to the cases in Goseong-gun and Cheorwon-gun, North Korea in April 2022. As a result, the suitability was confirmed as the FFDI at the time of ignition corresponded to the risk class Extreme and Severe sections, respectively. In addition, a qualitative comparison of the risk map and the soil moisture map before and after the wildfire, the correlation was grasped. A new forest fire risk index that combines drought factors such as soil moisture, Standardized Precipitation Index (SPI), and Normalized Difference Water Index (NDWI) will be needed in the future.

• Journal of the Korean Institute of Gas
• /
• v.22 no.2
• /
• pp.29-33
• /
• 2018

Accident likelihood is growing due to a correlation for gas and electricity installed in the area of dense energy consumption like traditional market and underground shopping center. In order to prevent and respond accident risks related to gas and electricity in this area, it should be monitored and predicted for factors of gas leak or electricity by developing safety management system. This study is about accident prediction model development considering fire risk factor related to gas accident. The temperature variation characteristic near a gas burner was analyzed. Also, accident prediction algorithm and related module were developed to prevent fire in the area of dense energy consumption.