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
Full-text
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
Due to the recent centralization of the metropolitan area, the number of vacant houses in local cities continues to increase. Accordingly, the government is conducting a survey on vacant houses, but the cost of on-site surveys is high due to the low accuracy of the preliminary survey. Therefore, in order to efficiently conduct an empty house survey, it is necessary to accurately find buildings suspected of empty houses in the preliminary survey stage and take follow-up measures accordingly.
This study aims to expand the efficiency of the survey of vacant houses by attempting to estimate vacant houses through artificial intelligence by using data on electricity, water usage, buildings, and socioeconomic variables to help early detection of vacant houses.
As a result, Decision Tree Ensemble Models showed the best performance based on Accuracy, F1-score, and AUC scores, and building data in addition to electricity usage were also identified as important variables in estimating empty houses.
This study aims to expand the efficiency of the survey of vacant houses by attempting to estimate vacant houses through artificial intelligence by using data on electricity, water usage, buildings, and socioeconomic variables to help early detection of vacant houses.
As a result, Decision Tree Ensemble Models showed the best performance based on Accuracy, F1-score, and AUC scores, and building data in addition to electricity usage were also identified as important variables in estimating empty houses.
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-ECN-0101-2022-324-001508742
