A hybrid model-based method for leak detection in large scale water distribution networks

During the past decades, the problem of finding leaks in Water Distribution Networks (WDN) has been challenging. The quicker detection of leaks, on one hand prevents water loss and helps avoiding economic and environmental leakage consequences. On the other hand, increasing the speed of leak detecti...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2021-02, Vol.12 (2), p.1613-1629
Main Authors: Fereidooni, Zahra, Tahayori, Hooman, Bahadori-Jahromi, Ali
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Artificial Intelligence
Bayesian analysis
Computational Intelligence
Decision trees
Engineering
Fiber optics
Fourier transforms
Leak detection
Leaks
Methods
Noise
Original Research
Partial differential equations
Pipelines
Pipes
Prediction models
Pressure drop
Robotics and Automation
Sensors
Sound waves
Topology
User Interfaces and Human Computer Interaction
Water distribution
Water engineering
Water loss
Wireless communications
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Summary:During the past decades, the problem of finding leaks in Water Distribution Networks (WDN) has been challenging. The quicker detection of leaks, on one hand prevents water loss and helps avoiding economic and environmental leakage consequences. On the other hand, increasing the speed of leak detection increases the false leak detection that imposes high costs. In this paper, we propose a fast hybrid method using AI algorithms and hydraulic relations for detecting and locating leaks and identifying the volume of losses material. The proposed method relies on simple and cost-effective flow sensors installed on each junction in the pipeline network. We demonstrate how influential features for leak detection would be generated by using hydraulic equations like Hazen–Williams, Darcy–Weisbach and pressure drop. Through exploiting Decision Tree, KNN, random forest, and Bayesian network we build predictive models and based on the pipeline topology, we locate leaks and their pressure. Comparing the results of applying the proposed method on various leak scenarios shows that the proposed method in this paper, outperforms other existing methods.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-02233-2