인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
학술저널
DBpia Top 10%
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
Purpose: This study proposes a novel approach to reduce the severe damages caused by factory fires in South Korea. The current fire risk assessment system faces limitations in providing detailed evaluations for factory buildings. This research utilizes public data and machine learning to swiftly and accurately predict fire risks in factories and seeks methods to identify and manage high-risk areas within industrial complexes. Method: The research process encompasses data collection, preprocessing, model prediction, and the integration of spatial data using GIS. It leverages building information provided by the national data portal and fire scenario data set as control variables. Data preprocessing includes the simplification of categorical variables, creation of derived variables, and the conversion of string data into numeric data. The predictive outcomes are integrated with spatial data using GIS, and industrial complexes are subdivided into blocks for risk level grading. This method aims to make a practical contribution to the management and prevention of fire risks in industrial complexes. Result: This study classified and analyzed the characteristics of factory buildings in aged three industrial complexes, assessing regional differences. Utilizing the Random Forest model, fire risks were categorized into low, medium, and severe levels, and regression analysis was employed to evaluate the impact of factors on fire risk. A five-tier grading system based on GIS visualization comprehensively represents the fire risk by region, offering valuable information for fire risk management. This research contributes to the development of policies aimed at enhancing safety in industrial complexes and minimizing property loss.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-24-02-089622448
