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

TL;DR

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.

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.

Figures (2)

• Figure 1: Overview of the particularities of and key tasks in WDNs (in the boxes) and their dependencies. Red connections mark key challenges, green potentials, and blue additional constraints.
• Figure 2: Schematic of approaches used in the literature