Multi-parameter multi-sensor data fusion for drinking water distribution system water quality management

Water quality events within drinking water distribution systems are commonly first detected by consumers. This undermines confidence, is in contrast to the level of service consumers now expect and in extreme cases can indicate a risk to public health. The objective of this paper is to propose and show how the value and insight gained increases in progression from single parameter, single location to multiple parameter multiple location sensor approaches. This is done using real-world datasets from operational systems and application of multiple analytical approaches in combination to extract actionable insights. Analytical methods include: cross-correlation for system connectivity and transit time derivation; an innovative turbidity event scale system; and material flux analysis of discolouration material. This research demonstrates the ability to confirm and track network-wide events, determine root causes, and inform proactive management via advisory event scores. The evidence provided of the multiplicative jumps in value in progressing towards multiple parameter multiple sensor approaches is vital to help guide future strategies for networks of water quality sensing.