Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
Abstract
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.
Cite
@article{arxiv.2410.12461,
title = {Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks},
author = {Valerie Vaquet and Fabian Hinder and André Artelt and Inaam Ashraf and Janine Strotherm and Jonas Vaquet and Johannes Brinkrolf and Barbara Hammer},
journal= {arXiv preprint arXiv:2410.12461},
year = {2024}
}
Comments
This preprint has not undergone any post-submission improvements or corrections. The Version of Record of this contribution is published in Artificial Neural Networks and Machine Learning -- ICANN 2024