Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
It addresses the problem of improving water management for utilities and communities facing climate change, but is incremental as it surveys existing methods rather than introducing new ones.
This survey paper examines the application of machine learning to water distribution networks, focusing on tasks like leakage detection and localization, and provides benchmarks and a structured analysis of domain-specific challenges and opportunities.
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.