스캔통계량 분석을 통한 상수도 누수 및 수질 민원 발생 클러스터 탐색
(주)코리아스칼라
- 최초 등록일
- 2023.11.20
- 최종 저작일
- 2023.10
- 9페이지/
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서지정보
ㆍ발행기관 : 대한상하수도학회
ㆍ수록지정보 : 상하수도학회지 / 37권 / 5호
ㆍ저자명 : 이주원, 김은주, 남숙현, 황태문
목차
1. 서 론
2. 연구방법
2.1 데이터 수집 및 처리
2.2 푸아송 시공간 스캔 통계
2.3 공간패턴 시각화
3. 결 과
3.1 시공간 클러스터
3.2 커널밀도추정
4. 결 론
사 사
영어 초록
In light of recent social concerns related to issues such as water supply pipe deterioration leading to problems like leaks and degraded water quality, the significance of maintenance efforts to enhance water source quality and ensure a stable water supply has grown substantially. In this study, scan statistic was applied to analyze water quality complaints and water leakage accidents from 2015 to 2021 to present a reasonable method to identify areas requiring improvement in water management. SaTScan, a spatio-temporal statistical analysis program, and ArcGIS were used for spatial information analysis, and clusters with high relative risk (RR) were determined using the maximum log-likelihood ratio, relative risk, and Monte Carlo hypothesis test for I city, the target area. Specifically, in the case of water quality complaints, the analysis results were compared by distinguishing cases occurring before and after the onset of "red water." The period between 2015 and 2019 revealed that preceding the occurrence of red water, the leak cluster at location L2 posed a significantly higher risk (RR: 2.45) than other regions. As for water quality complaints, cluster C2 exhibited a notably elevated RR (RR: 2.21) and appeared concentrated in areas D and S, respectively. On the other hand, post-red water incidents of water quality complaints were predominantly concentrated in area S. The analysis found that the locations of complaint clusters were similar to those of red water incidents. Of these, cluster C7 exhibited a substantial RR of 4.58, signifying more than a twofold increase compared to pre-incident levels. A kernel density map analysis was performed using GIS to identify priority areas for waterworks management based on the central location of clusters and complaint cluster RR data.
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