Algorithms to mimic human interpretation of turbidity events from drinking water distribution systems

Abstract

Deriving insight from the increasing volume of water quality time series data from drinking water distribution systems is complex and is usually situation- and individual-specific. This research used crowd-sourcing exercises involving groups of domain experts to identify features of interest within turbidity time series data from operational systems. The resulting labels provide insight and a novel benchmark against which algorithmic approaches to mimic the human interpretation could be evaluated. Reflection on the results of the labelling exercises resulted in the proposal of a turbidity event scale consisting of advisory <2 NTU, alert 2 < NTU < 4, and alarm >4 NTU levels to inform utility response. Automation was designed to enable event detection within these categories. A time-based averaging approach, calculating averages based on data at the same time of day, was found to be most effective for identifying low-level (<2 NTU) events. Simple flat-line event detection was sufficient to identify higher-level alert and alarm events. The automation of event detection and categorisation presented here provides the opportunity to gain actionable insight to safeguard drinking water quality from aging infrastructure.