한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제54권8호
  • /
  • Pages.599-606
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  • 2021
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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LSTM 기반 딥러닝 알고리즘을 적용한 상수도시스템 누수인지 모델 개발

Development of leakage detection model in water distribution networks applying LSTM-based deep learning algorithm

  • 이찬욱 (수원대학교 토목공학과) ;
  • 유도근 (수원대학교 토목공학과 하천환경기술연구소)
  • Lee, Chan Wook (Department of Civil Engineering, The University of Suwon) ;
  • Yoo, Do Guen (Institute of River Environmental Technology, Department of Civil Engineering, The University of Suwon)
  • 투고 : 2021.05.31
  • 심사 : 2021.06.16
  • 발행 : 2021.08.31
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초록

지하에 매설되어 있는 사회기반시설물 중 하나인 상수도시스템은 정수처리된 물을 수용가에게 수송 및 공급하는 기능을 가지고 있다. 최근들어, 계측능력이 향상됨에 따라 유량데이터에 의한 딥러닝기법을 적용한 누수 인지 및 탐지와 관련한 연구가 다수 수행되고 있다. 본 연구에서는 현재까지 상수도 분야에 적용되지 않은 LSTM 기반의 딥러닝 알고리즘을 활용하여 누수발생에 대한 인지 모형을 개발하였다. 가정한 데이터를 기반으로 모형에 대한 검증을 수행하였으며 2% 이상의 누수가 발생한 경우에 대하여 모두 인식이 가능한 것으로 나타났다. 향후, 제안된 모형을 토대로 유량 데이터 예측부분에 있어서 보다 정밀한 결과가 도출 될 수 있을것으로 판단된다.

Water Distribution Networks, one of the social infrastructures buried underground, has the function of transporting and supplying purified water to customers. In recent years, as measurement capability is improved, a number of studies related to leak recognition and detection by applying a deep learning technique based on flow rate data have been conducted. In this study, a cognitive model for leak occurrence was developed using an LSTM-based deep learning algorithm that has not been applied to the waterworks field until now. The model was verified based on the assumed data, and it was found that all cases of leaks of 2% or more can be recognized. In the future, based on the proposed model, it is believed that more precise results can be derived in the prediction of flow data.

키워드

과제정보

본 결과물은 환경부의 재원으로 한국환경산업기술원의 지능형 도시수자원 관리사업의 지원을 받아 연구되었습니다(2019002950002). 이에 감사드립니다.

참고문헌

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