한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권7호
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  • Pages.963-971
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  • 2022
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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관망자료를 이용한 인공지능 기반의 누수 예측

Artificial Intelligence-based Leak Prediction using Pipeline Data

  • 투고 : 2022.05.23
  • 심사 : 2022.06.16
  • 발행 : 2022.07.31
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초록

상수도 관망은 국가 수도 시설의 주요한 구성 요소이지만 대부분이 지중에 매립되어 있어 배관의 노후화 정도 및 누수를 파악하기 어려우므로 유지관리 하기가 매우 어렵다. 본 연구에서는 관망에 설치된 다양한 센서 조합을 가정하여, 데이터 조합에 따른 관로 누수 판별 가능성을 검토하기 위하여 선형회귀분석, 뉴로퍼지 등의 인공지능 알고리즘을 통한 유량과 압력 예측을 실시하여 최적 알고리즘을 도출하였다. 공급압력 예측을 통한 누수판별의 경우 뉴로퍼지 알고리즘이 선형회귀분석에 비하여 우수하였다. 누수유량 예측에서는 뉴로퍼지를 이용한 유량예측이 우선 고려되어야 한다. 다만, 유량을 모사하기 힘든 경우에는 선형 알고리즘을 이용한 공급압력 예측이 이루어져야 할 것으로 사료 된다.

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

키워드

과제정보

This work was supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government(MOTIE) (No. 20202000000010.)

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