• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.4
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• pp.31-39
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• 2022

The purpose of this study is to analyse the hazard risk by examining the magnitude and severity of each type of hazard in order to mitigate and prepare for disasters in medical facilities. Methods: The hazard risk analysis for hazard types was surveyed for team leaders of medical facilities. The questionnaire analyzed data from 27 facilities, which were returned from 41 Local Medical Centers. Results: When looking at the 'Risk' by category type of hazard, the influence of health safety and fire/energy safety comes first, followed by natural disaster, facility safety, and crime safety. On the other hand, as for 'Magnitude', facility safety and crime safety come first, followed by health safety, fire/energy safety, and natural disasters. Most of the top types of disaster judged to have high hazard in medical facilities are health types. The top five priorities of hazard in medical facilities, they are affected by the geographical and industrial conditions of the treatment area. In the case of cities, the hazard was found to be high in the order of infectious disease, patient surge, and wind and flood damage. On the other hand, in rural areas, livestock diseases and infectious diseases showed the highest hazard. In the case of forest areas, the hazard was high in the order of wildfire, fire accident, lightning, tide, earthquake, and landslide, whereas in coastal areas of industrial complexes, the hazard was high due to fire, landslide, water pollution, marine pollution, and chemical spill accident. Implications: Through the research, standards will be established for the design of hospitals with disaster preparedness, and will contribute to the preparation of preemptive measures in terms of maintenance.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.414-414
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• 2023

지구온난화로 인한 기후변화로 산불 발생이 증가하고 있는 추세이다. 이로 인해 무강우기간의 증가, 기온의 상승, 습도 감소 등의 문제가 발생하고 있다. 이중 기온의 상승과 습도의 감소는 산불과 큰 연관성이 있다. 따뜻하고 건조한 날씨는 토양수분의 감소로 이어지며, 이는 곧 식물의 활성도의 감소로 이어진다. 봄철 식물의 활성도가 감소하게 되면 자연스레 산불 발생 위험도가 높아지기 마련이다. 이러한 현상은 국내에서도 관찰 가능하다. 따뜻하고 건조한 국내 겨울철 날씨는 봄철 토양수분의 감소와 직결되며, 건조해진 토양수분으로 인해 봄철인 3월~5월 산불 발생 확률이 증가하게 된다. 실제로 최근 10년간 국내에서 산불이 많이 발생한 계절은 봄철이며, 산불로 인한 피해 면적 및 피해 금액이 가장 큰 기간도 봄철이다. 따라서 봄철 산불에 대한 각별한 주의가 요구 되어지고 있다. 본 연구에서는 국내 유역을 대상으로 산불 위험도 지수에 대한 평가를 진행하고자 한다. 이에 PRMS 모형을 이용해 봄철 토양수분을 모의하여 산불 위험도 지수와 토양수분의 상관성을 나타내고자 한다. 또한, 산불 발생 건수를 활용하여 산불 위험도 지수에 대한 적절성을 평가하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.54-57
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• 2022

Korea has a vulnerable environment from the risk of wildfires, which causes great damage every year. To prevent this, a lot of manpower is being used, but the effect is insufficient. If wildfires are detected and extinguished early through artificial intelligence technology, damage to property and people can be prevented. In this paper, we studied the object double detection method with the goal of minimizing the data collection and processing process that occurs in the process of creating an object detection model to minimize the damage of wildfires. In YOLOv5, the original image is primarily detected through a single model trained on a limited image, and the object detected in the original image is cropped through Crop. The possibility of improving the false positive object detection rate was confirmed through the object double detection method that re-detects the cropped image.

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