Leakages are undesirable events in water distribution networks (WDNs) with mostly
unknown incident time making it extremely difficult to accurately label leaks in distribution …

This paper explores the use of 'conditional convolutional generative adversarial
networks'(CDCGAN) for image-based leak detection and localization (LD&L) in water …

This paper explores the use of deep learning for leak localization in Water Distribution
Networks (WDNs) using pressure measurements. By using a training data set including …

Abstract Currently, a total of 3.6 billion people live in water-deficient areas, and the
population living in water-deficient areas may reach from 4.8 to 5.7 billion by 2050. Despite …

Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon
of water annually in the US and cause significant socio-economic loss to our communities …

Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in
water distribution systems (WDSs) can cause the loss of a large amount of treated water …

Leaks represent one of the most relevant faults in water distribution networks (WDN),
resulting in severe losses. Despite the growing research interest in critical infrastructure …

The water supply network (WSN) is subjected to leaks that compromise its service to the
communities, which, however, is challenging to identify with conventional approaches …

In the water supply network, leakage of pipes will cause water loss and increase the risk of
environmental pollution. For water supply systems, identifying the leak point can improve the …

Leak detection and localization in water distribution networks (WDNs) is of great significance
for water utilities. This paper proposes a leak localization method that requires hydraulic …