SensorClean.ai articles

The global direct cost of non-revenue water (NRW) is 39 billion US dollars per year (Liemberger and Wyatt 2019).

In large cities, reducing a small percentage of water loss can save tens of millions of dollars by deferring capital investment for new sources of water, such as desalination plants.  

NRW is a significant financial loss, uses valuable maintenance resources and doesn’t bode well in times of extreme drought and climate change.  

To inform main

In stormwater design, frequency curves are used to determine return periods for the design of stormwater infrastructure. For instance, a stormwater pipe may have a design return period for a 1 in 20-year rainfall event.

With a frequency curve, engineers can do risk analysis, comparing the annual savings from mitigated floods (benefits) against various levels of flood mitigation infrastructure (costs).

In the design of water networks ‘peaking factors’ are used for simulating design flows in hydraulic models.

In simple terms, the dimensionless peaking factor is maximum over average consumption.

Historically, utilities adopted standardized peaking factors based on their source data (treatment plant etc.) which was the most reliable source of data.

But as we enter the world of big data - and move beyond spreadsheet limitations - new possibilities arise to an

This post explores why we need data consistency across utilities and how we might maintain it with shared workflows.

In use-case workflows, there are two processing stages before analysing water data.

The first of these is basic cleaning – taking the raw data and removing unreadable data and duplicates, erroneous data spikes and getting timestamps into correct sequence.

These processes can be automated except for the removal of erroneous data ‘spikes’

Water utilities have long understood the potential value of the 20+ years of SCADA monitoring data which they store.

SCADA data provides valuable context about asset performance, changing community water behaviours, and the effectiveness of utility strategies, amongst many other things.

But SCADA data is messy and disorganized and utilities have been hamstrung by inadequate data cleaning tools.  

We saw the need for a tool to leverage the treasure-trove of SCADA d

Cleaned historical reservoir data tells a story about water age, which impacts on water quality. Excessive water ageing is likely to lead to a reduction in residual chlorine concentration, and potentially poor disinfection for communities.

Reservoirs can experience considerable differences in winter and summer water age especially where there is high summer tourism.

Cleaning and organising historical reservoir data can give practitioners a useful overview of water age at diffe

It is well known that training machine learning models under supervised conditions requires significant data that is labelled in a systematic way. Once operational, these models can provide significant value to water utilities and communities.

Though, what may not be so clear is the immediate benefits of labelling your water data. This post endeavours to shed some light on this topic. But first, a brief overview of data collection on water and wastewater networks.

Over the las