Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.

1. 서 론
2. 연구방법
    2.1 물수지 분석 기반 관파손감지 절차
    2.2 분석범위 설정, 자료 수집 및 전처리
    2.3 물수지 분석 모델 구축
    2.4 물수지 분석 모델기반 관파손감지 성능평가
3. 결 과
    3.1 물수지 분석 모델
    3.2 물수지 분석 모델 기반 관파손감지 성능평가
4. 결 론
References

• 신은허(K-water 연구원) | Eunher Shin (K-water Research Institute, K-water) Corresponding Author
• 정기문(K-water 연구원) | Gimoon Jeong (K-water Research Institute, K-water)
• 김경필(K-water 연구원) | Kyoungpil Kim (K-water Research Institute)
• 최태호(K-water 연구원) | Taeho Choi (K-water Research Institute)
• 채선하(K-water 연구원) | Seon-ha Chae (K-water Research Institute)
• 조용우(K-water 한강유역본부) | Yong Woo Cho (Hangang River Basin Head Office, K-water)