Abstract
Water distribution systems are made up of interconnected components that should allow water systems to meet demand, but leaks can waste enough water to limit supply. To limit financial losses, water utilities must quickly determine that a leak is occurring and where it is referred to as the localization of the leak. Over the years, there have been various methods proposed to detect and locate leaks. This literature review summarizes many of the methodologies introduced, categorizes them into data-driven approaches and model-based methods, and reviews their performance. Data-driven approaches demand efficient exploitation and use of available data from pressure and flow devices, and model-based methods require finely calibrated hydraulic models to reach a verdict. Data-driven approaches can manage uncertainty better than model-based methods.
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Abbreviations
- ACO:
-
Ant colony optimization
- ANN:
-
Artificial neural network
- AWWA:
-
American Water Works Association
- BLIFF:
-
Burst location identification framework by fully linear DenseNet
- BN:
-
Bayesian network
- BPNN:
-
Back-propagation neural network
- Cas-SVDD:
-
Cascade support vector data description
- CNN:
-
Convolutional neural network
- CtL-SSL:
-
Clustering-then-localization semi-supervised learning
- CUSUM:
-
Cumulative sum
- DenseNet:
-
Densely connected convolutional networks
- DL:
-
Deep learning
- DMA:
-
District metering areas
- EM:
-
Expectation maximization
- EWMA:
-
Exponential weighted moving average
- ExSem:
-
Expert structural expectation–maximization
- FLS:
-
Fuzzy logic system
- FNR:
-
False-negative rate
- FPR:
-
False-positive rate
- IDW:
-
Inverse distance weighted
- ISLMD:
-
Improved spline-local mean decomposition
- IWA:
-
International water association
- KPCA:
-
Kernel principal component analysis
- LDA:
-
Linear discriminant analysis
- LKF:
-
Linear Kalman filter
- LMD:
-
Local mean decomposition
- LP:
-
Local polynomial
- LS:
-
Least squares
- LSS:
-
Leak signature space
- LSTM:
-
Long short-term memory
- MDN:
-
Mixture density network
- MLPNN:
-
Multilayer perceptron neural network
- MDN:
-
Mixture density network
- MNF:
-
Minimum night flow
- NKF:
-
Nonlinear Kalman filter
- NRW:
-
Non-revenue water
- OC:
-
Ordinary cokriging
- OK:
-
Ordinary kriging
- PCA:
-
Principal component analysis
- PKF:
-
Predictive Kalman filter
- RMSE:
-
Root-mean-square error
- RNN:
-
Recurrent neural network
- SCADA:
-
Supervisory control and data acquisition
- SIP:
-
Standardized innovation process
- SOM:
-
Self-organizing maps
- SPC:
-
Statistical process control
- SVR:
-
Support vector machine
- TDOA:
-
Time difference of arrival
- TFR:
-
Transient frequency response
- TNR:
-
True-negative rate
- TPR:
-
True-positive rate
- WDS:
-
Water distribution system
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Acknowledgements
This project was supported by the Smart Cities Innovation Partnership Program (Award Number 134146) of the New York State Empire State Development (ESD).
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Nimri, W., Wang, Y., Zhang, Z. et al. Data-driven approaches and model-based methods for detecting and locating leaks in water distribution systems: a literature review. Neural Comput & Applic 35, 11611–11623 (2023). https://doi.org/10.1007/s00521-023-08497-x
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DOI: https://doi.org/10.1007/s00521-023-08497-x