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2024 Vol.57, Issue 12 Preview Page

Research Article

Prediction of real-time data in water distribution systems using LSTM

LSTM을 이용한 상수도 시스템 실시간 데이터 예측

Eunyoung Choa
,
Seonhong Choib
,
Hanna Jungc
,
Dongwoo Jangd*
조 은영a
,
최 선홍b
,
정 한나c
,
장 동우d*
aMaster’s Course, Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea
bMaster’s Course, Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea
cMaster’s Course, Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea
dAssociate Professor, Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea
a인천대학교 건설환경공학과 석사과정
bMaster’s Course, Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea건설환경공학과 석사과정
c인천대학교 건설환경공학과 석사과정
d인천대학교 건설환경공학과 부교수
*Corresponding Author
31 December 2024. pp. 1003-1014
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Abstract

The domestic water supply coverage rate exceeds 99%, and as aging water pipes increase, the risk of hydraulic and water quality incidents in water pipelines is rising, making it urgent to establish countermeasures. To prepare for various incidents in water supply systems, such as red water and larval accidents, real-time monitoring devices have recently been installed in pipelines. Consequently, the importance of research utilizing the measured data is gradually increasing. In this study, the Long Short-Term Memory (LSTM) method was used to predict future hydraulic and water quality data within water pipelines. Two months of hourly real-time measurement data, including flow discharge, hydraulic pressure, residual chlorine, and pH, were collected from a small block in Seo-gu, Incheon. The predictive accuracy of the LSTM technique was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results of predicting water supply factors over the next seven days showed a high correlation, with R2 reaching up to 0.91 for flow discharge. Electrical conductivity, water temperature, and residual chlorine also demonstrated high accuracy, with R2 values exceeding 0.8. However, hydraulic pressure showed a low R2 value of 0.019, suggesting that the predictive accuracy of the LSTM model is influenced by the continuity of the data used. The prediction results of real-time measurement data through LSTM can serve as crucial information for water supply management and incident prevention, and are expected to improve the ability to respond to incidents effectively.

Keywords
Data prediction
LSTM
Machine learning
Real-time data
Water supply systems

국내 수도관의 보급률은 99% 이상이며, 이에 노후 수도관이 증가하고 있고, 상수관로 내 수리 및 수질 사고의 발생 위험을 높여 대책 마련이 시급한 실정이다. 적수 및 유충 사고 등 상수도에서 발생할 수 있는 다양한 사고에 대비하기 위하여 최근 실시간 모니터링 계측기가 관로에 설치되고 있고, 계측된 데이터를 활용한 연구의 중요성이 점차 증가하고 있다. 본 연구에서는 LSTM (Long Short-Term Memory) 방법을 이용하여 미래 상수도관 내 수리 및 수질 데이터를 예측하고자 하였다. 인천광역시 서구 소블럭의 유량, 수압, 잔류염소, pH 등 2개월의 시 단위 실시간 계측 데이터를 수집하였고, 결정계수와 RMSE (Root Mean Square Error)를 이용하여 LSTM 기법의 예측 정확도를 평가하였다. 장래 7일간의 상수도 인자별 데이터를 예측한 결과, 유량의 경우 R2가 최대 0.91로 높은 상관성을 보이는 것으로 나타났으며, 전기전도도, 수온, 잔류염소의 경우 0.8 이상의 값으로 높은 정확도를 보여주었다. 반면 수압의 경우 0.019의 낮은 값을 보여주었는데, 이는 LSTM 모델은 활용하는 데이터의 연속성 유무에 따라 예측 정확도에 영향을 받는 것으로 시사된다. LSTM을 통한 실시간 계측 데이터의 예측 결과는 상수도 관리와 사고 예방에 중요한 정보로 활용될 수 있으며, 사고 발생 시 대응 능력을 향상시키는 데 도움이 될 것으로 기대된다.

키워드
데이터 예측
LSTM
머신러닝
실시간 데이터
상수도
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 57
  • No :12
  • Pages :1003-1014
  • Received Date : 2024-10-03
  • Revised Date : 2024-10-29
  • Accepted Date : 2024-11-07
  • DOI :https://doi.org/10.3741/JKWRA.2024.57.12.1003
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