# Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks

arXiv: https://arxiv.org/abs/2410.12461 · HTML: https://mlsanity.com/paper/2410.12461/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 53 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [An Algorithm-Centered Approach To Model Streaming Data](https://mlsanity.com/paper/2412.09118/) (2024)
- [Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems](https://mlsanity.com/paper/2502.12164/) (2025)
- [Water Quality Estimation Through Machine Learning Multivariate Analysis](https://mlsanity.com/paper/2512.02508/) (2025)

## Abstract (arXiv)

Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization.