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Leak detection and localization in water distribution networks using conditional deep convolutional generative

Mohammad Mahdi Rajabi1, Pooya Komeilian2, Xi Wan3

  • 1Civil and Environmental Engineering Faculty, Tarbiat Modares University, PO Box 14115-397, Tehran, Iran.

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|May 7, 2023
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Summary

This study introduces a novel image-based method using conditional convolutional generative adversarial networks (CDCGAN) for detecting and locating leaks in water distribution networks. The approach achieves approximately 70% accuracy in real-time leak detection.

Keywords:
Anomaly detectionGenerative adversarial networksImage-to-image translationLeakStructural similarity indexWater Distribution

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Area of Science:

  • Engineering
  • Computer Science
  • Environmental Science

Background:

  • Water distribution networks (WDNs) are critical infrastructure prone to leaks.
  • Traditional leak detection methods can be time-consuming and costly.
  • Accurate and timely leak detection and localization (LD&L) are essential for water resource management.

Purpose of the Study:

  • To develop and evaluate an image-based method for real-time leak detection and localization in WDNs.
  • To leverage conditional convolutional generative adversarial networks (CDCGAN) for enhanced LD&L.
  • To assess the method's accuracy, robustness, and scalability.

Main Methods:

  • Utilized hydraulic model-based generation of leak-free training data considering demand uncertainty.
  • Converted hydraulic model input/output data into images via kriging interpolation.
  • Trained a CDCGAN (pix2pix architecture) for image-to-image translation.
  • Employed the structural similarity (SSIM) index for leak detection and localization.

Main Results:

  • The proposed CDCGAN-based method achieved approximately 70% accuracy for real-time leak detection.
  • Demonstrated effectiveness across various leakage scenarios and datasets.
  • The image-based approach proved robust to uncertainty and scalable for large datasets.

Conclusions:

  • The CDCGAN method offers a promising solution for efficient and accurate LD&L in WDNs.
  • The approach is well-suited for real-time applications due to low computational cost.
  • The method's robustness and scalability make it valuable for managing complex water networks.