Yorkshire Water begins real time AI water quality project at designated bathing water sites

Yorkshire Water, in collaboration with AI specialists UnifAI Technology, has launched a two-year research project to deliver real-time water quality predictions at designated bathing water sites across the county. The initiative combines current flow and water quality monitoring with AI modelling to provide predictive insights for public health and recreational decisions.

Project scope and methodology

The project uses SOCOTEC monitoring sensors to collect readings every 15 minutes at 20 inland bathing water sites, measuring dissolved oxygen, pH, ammonia, temperature and turbidity. Water samples are taken four times per week at each site and lab-tested for harmful bacteria, including E. coli and Enterococci. More than 7,800 data sets are expected over 24 months to train a predictive model capable of estimating bacteria levels in near real time.

The approach combines live sensor data with lab results to train machine-learning models and aims to produce a transferable, site-agnostic framework for real-time monitoring across river, loch, lido and coastal inlets. The program is funded through Ofwat’s Water Breakthrough Challenge 5 Catalyst stream, with partners UnifAI Technology, The Rivers Trust and BSI, and with support from Southern Water and SOCOTEC.

Dan Byles, Chief Commercial Officer of UnifAI Technology, said: "Current water quality monitoring relies on periodic sampling and laboratory testing. Although essential, these processes are time-consuming and only provide a historic picture of conditions rather than what is happening at the moment people enter the water. As a result, the public often learns about potential health risks after the fact. The UnifAI Technology and Yorkshire Water research collaboration is looking to accelerate this process through AI learning."

Isabell Holling, Managing Director of Monitoring & Surveying at SOCOTEC UK & Ireland, said: "We are pleased to support this groundbreaking project by deploying our specialist environmental monitoring technology across 20 appointed inland bathing water sites. The precision and reliability of continuous water quality data is fundamental to the success of this AI-driven approach. Our equipment will provide the robust, high-frequency measurements needed to train predictive models that can truly protect public health. This collaboration demonstrates how advanced monitoring technology and artificial intelligence can work together to transform environmental management and deliver real-time insights that benefit both communities and regulators."

The program builds on proven AI deployments at Warleigh Weir and Bournemouth Boscombe, where early models have achieved 87% accuracy. Yorkshire Water aims to create a transferable template for real-time water quality monitoring that can be deployed at scale across water types.

A key objective is data openness. If successful, near-live water quality predictions will be accessible to the public via a user-friendly web app, and regulators and partners will gain real-time insights to support environmental protection, planning and investment.

Sites involved

• Ilkley
• Wetherby
• Knaresborough
• Masham
• Burley in Wharfedale
• Harrogate North
• Springfield Avenue, Bridlington
• Doncaster Rowing Club
• Dowley Gap
• Scalby Beck

Timeline, regulatory context and priorities

The initiative spans 24 months, after which model accuracy will be evaluated. The project does not replace ongoing investment to improve water quality and reduce pollution. It also supports forthcoming Section 82 requirements under the Environment Act for continuous water quality monitoring upstream and downstream of all combined sewer overflows by 2035.

Faye Cossins, Coastal Delivery & Engagement Manager at Yorkshire Water, stated: "We know that people are passionate about their Yorkshire rivers and waterways, and they rightly want clearer, quicker information about water quality. This project has real potential to give communities near real-time insights so they can make confident, informed decisions about taking a dip. We’re pleased to be leading this work with our partners and with funding from Ofwat’s Breakthrough Challenge. It’s an important step forward in innovation, transparency and preparing for future environmental monitoring requirements."

Data openness and accessibility are central to the plan, with near-live predictions intended for public use via a web app and real-time insights available to regulators and partners for environmental protection, planning and investment.