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GIS를 활용한 고속도로 염화수소 가스 누출 시나리오 기반 리스크 평가
김구윤 ( Kuyoon Kim ),이재준 ( Jaejoon Lee ),윤홍식 ( Hongsik Yun ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.3
국내의 화학 산업이 지속적으로 발전이 이루어짐에 따라 화학물질의 취급량과 운송량은 매년 증가하고 있다. 우리나라 도로 화물운송은 90%이상을 차지하고 있으며, 화학물질 운송도 대부분 도로를 통해 이루어지고 있다. 이러한 화학물질 운송차량들은 사고가 발생하게 되면 대형사고로 이어질 수 있다. 운송차량은 1차 피해인 교통사고뿐만 아니라 2차 피해인 환경 피해 요인들인 수질오염, 토양오염 등을 발생시킬 가능성이 높다. 본 연구는 반포IC와 서초IC 구간을 연구지역으로 설정하여 염화수소 가스 누출에 대한 시나리오를 작성하여, ALOHA 프로그램을 사용하여 예측거리를 측정하고 거리에 따라 염화수소 가스가 도달한 시간을 분석하였다. 또한 GIS를 이용해 시간별로 발생한 피해 영역에 대해서 인구밀도를 이용한 리스크 평가를 수행하였다. 이를 통해 피해 영역에 대해서 예방·대응 방안의 필요성을 제시하였다. As the domestic chemical industry continues to develop, handling and transportation of chemicals increases every year. Road freight in Korea accounts for more than 90%, and most of the chemical transportation is done through roads. These chemical vehicles can lead to major accidents if accidents occur. Transportation vehicles are likely to cause water pollution and soil pollution, which are factors of environmental damage, as well as traffic accidents that are the primary damage. In this work, we write a scenario for hydrogen chloride gas leakage by setting Banpo IC and Seocho IC sections as research areas, and use the ALOHA program to measure the predicted distance and analyze the time when hydrogen chloride gas reached according to the distance. In addition, risk assessment using population density was carried out for areas of damage caused by time using GIS. This suggests the need for prevention and countermeasures in areas of damage.
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동해안 산불피해 사례기반 격자체계를 활용한 산불위험분석
김구윤,이미란,곽창재,한지혜,Kuyoon Kim,Miran Lee,Chang Jae Kwak,Jihye Han 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
Recently, forest fires have become frequent due to climate change, and the size of forest fires is also increasing. Forest fires in Korea continue to cause more than 100 ha of forest fire damage every year. It was found that 90% of the large-scale wildfires that occurred in Gangwon-do over the past five years were concentrated in the east coast area. The east coast area has a climate vulnerable to forest fires such as dry air and intermediate wind, and forest conditions of coniferous forests. In this regard, studies related to various forest fire analysis, such as predicting the risk of forest fires and calculating the risk of forest fires, are being promoted. There are many studies related to risk analysis for forest areas in consideration of weather and forest-related factors, but studies that have conducted risk analysis for forest-friendly areas are still insufficient. Management of forest adjacent areas is important for the protection of human life and property. Forest-adjacent houses and facilities are greatly threatened by forest fires. Therefore, in this study, a grid-based forest fire-related disaster risk map was created using factors affected by forest-neighboring areas using national branch numbers, and differences in risk ratings were compared for forest areas and areas adjacent to forests based on Gangneung forest fire cases.
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격자 기반 침수위험지도 작성을 위한 기계학습 모델별 성능 비교 연구 - 2016 태풍 차바 사례를 중심으로 -
한지혜,곽창재,김구윤,이미란,Jihye Han,Changjae Kwak,Kuyoon Kim,Miran Lee 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.
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