A novel method to both assess the strength of connectivity and determine hydraulic transit times between water quality monitors from time series data is reported. It was developed using a network of over 50 mobile multi-parameter sensors deployed for 18 months across a UK drinking water distribution system, and then validated using a network of 18 sensors from a different UK utility. Correlation coefficients are calculated at different time shifts for each possible sensor pair, with strength of connectivity represented by the highest correlation coefficient, and with the temporal lag of this highest correlation also designates the transit time. The results demonstrate the potential to derive valuable spatio-temporal information, with potential to increase understanding of system performance and connectivity. This information can be used to assist with further analytics such as tracking water quality events and improving hydraulic and disinfection residual decay modelling.

The derivation of information from monitoring drinking water quality at high spatiotemporal resolution as it passes through complex, ageing distribution systems is limited by the variable data quality from the sensitive scientific instruments necessary. A framework is developed to overcome this. Application to three extensive real-world datasets, consisting of 92 multi-parameter water quality time series of data taken from different hardware configurations, shows how the algorithms can provide quality-assured data and actionable insight. Focussing on turbidity and chlorine, the framework consists of three steps to bridge the gap between data and information; firstly, an automated rule-based data quality assessment is developed and applied to each water quality sensor, then, cross-correlation is used to determine spatiotemporal relationships and finally, spatiotemporal information enables multi-sensor data quality validation. The framework provides a method to achieve automated data quality assurance, applicable to both historic and online datasets, such that insight and actionable insight can be gained to help ensure the supply of safe, clean drinking water to protect public health.