Machine Learning for the Water Sector

The water sector is currently facing massive challenges from climate change, shifting demographics and regulation. Our ML solutions for the water sector utilise cutting-edge machine learning techniques to enable confident decision-making.


Engaging with Leaders from the Water Sector

Digital Transformation

Water security, quality, and sustainability are the defining ESG challenges for the water sector. digiLab are experts in supporting organisations to apply ML across their systems, in order to fully leverage their data, optimise their activities, and deliver upon these challenges while minimising costs.

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twinLab Platform

twinLab, our intuitive ML platform, enables analysts and engineers to augment the intelligence provided by sensors and other data sources. Through seamless integrations, twinLab brings the power of probabilistic ML to challenges like site locating, network prediction, and asset monitoring.

SenSiteUQ - Sensor Location Optimisation Built in Partnership with OfWat

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SenSiteUQ - Sensor Location Optimisation Built in Partnership with OfWat

Use ML to minimise costs and maximise information

digiLabs innovative approach to using the data from cutting-edge sensors enabled us to discover how Machine Learning can gain us energy and cost savings, whilst also driving efficiency and water quality.

Nicholas Dade
Principal Scientist
South West Water

Get in touch to explore our technical demos:

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Coagulation Efficiency

Reducing coagulant requirements by 40%, improving water quality, and inferring Zeta potential based on the most cost effective sensors to reduce capex.

Control Optimisation for Attenuation Tanks

Using both upstream and downstream conditions to inform pump behaviour policies, minimising the duration and extent of downstream spillage.

Sensor Placement

SenSiteUQ places sensors optimally across a network and delivers improved data quality using the right number of sensors.

Biodiversity and Reef Management

Using computer vision to automatically predict the health of coral reefs, by extracting information on measurements and colour from raw, unlabelled video footage.

How twinLab Powers SenSite UQ

twinLab uses the latest in probabilistic modelling to represent your digital twin with built-in uncertainty quantification (UQ).
Seamless integration of network models, geospatial data, such as IOT connectivity, and surface conditions.
Round the clock technical support, training & workshops, and extensive domain specific demos & tutorials.