We do not often associate artificial intelligence (AI) with helping to tackle climate change, and certainly not how to drive environmental benefits, especially in the water industry. Climate change considerations are top of mind for the water industry as this affects freshwater sources. Alongside this, customer demand for water is rising and populations are growing. This is increasing pressure on water infrastructure.
In order to ensure that water companies are resilient and sustainable, they must make sure that their operations are fit for purpose now and in the future. To address this, it is crucial for the industry to fully embrace the opportunity that technology and AI-driven operations presents. Anglian Water is doing just that with their Safe Smart Systems Project. In collaboration with 27 partners, the project leverages AI to improve the long-term operational resilience of clean water systems for the rest of the industry and wider groups, and is on the journey to enable AI to predict water leaks.
The current water crisis
Globally, water scarcity is a growing concern, and this issue is becoming even more urgent as the population in the UK is projected to increase by 7.3 percent to 72.5 million people between mid-2022 to mid-2032. This puts a further strain on water resources and infrastructure. In addition, 19 percent of the water that was put into supply between 2023 to 2024 leaked before it entered customer properties. Even though the industry continues its great work in reducing leakage, with a 3.7 percent reduction in total leakages in the UK between 2023 to 2024 nationally, total leakage was still 2,690 Ml/d. This is above the forecast set out in the statutory water resources management plans (WRMPs), highlighting the difficulty of the challenge.
It is crucial for these challenges be addressed to create an environmentally sustainable water supply. There should be a move from reactive measures, where issues, like leaks, are only addressed and solved once it becomes severe, to one which is proactive. In this, AI-powered systems could predict leaks, for example, before they happen, and automate any adjustments that are needed to the network to address the issue. This will ensure a more sustainable water supply and an efficient distribution of water across regions and the country.
The technology behind sustainable water management
Alongside this, the technology can also be leveraged to automate operations to continually optimise water systems or where irregularities in the network are detected. It can automatically change parameters, such as pressures or levels and re-route water flow to minimise disruption to customers’ access to water.
Furthermore, the industry should invest in digital twins for their networks. These representations of the physical world are created through near real-time and automated data. By utilising secure interoperability of data, digital twins integrate extensive data from various sources, including sensors in the water networks.
With this, water companies and engineers have the ability to continuously monitor systems, meaning that AI can predict and prevent issues. This is done through detecting anomalies in customer usage patterns, pressure changes and near real time data collected from sensor readings. Through proactively identifying risks, water companies can carry out maintenance before an issue becomes significant and impacts customers.
With a digital twin model, engineers will also be able to simulate how an intervention, such as undertaking maintenance or fixing a leak, will impact the water network. Once the intervention is tested using the digital twin, the operation can then be applied to the physical network. Through proactive intervention and testing how operations will impact the network, water companies can ensure that they do not disrupt the flow of water to customers and minimise wastage.
The future of the water industry
Going further, with high levels of automation, the water industry could support self-maintaining water networks. These systems will be capable of detecting and addressing issues autonomously utilising real time sensor data and advanced AI decision engines.
A self-maintaining network will ensure optimal operation, detecting when anomalies occur through continuous monitoring. Then, once an issue is identified, it will analyse sensor data, as well as looking at historical patterns and weather conditions to determine the severity of the issue and understanding what is required to overcome it. Finally, the system will autonomously take the necessary action to solve the issue. For example, if a leak is detected in real time, the network would automatically reconfigure itself to minimise or mitigate the issue by diverting the water supply to minimise water waste. This would not require human intervention, enabling engineers to focus on more complex issues. The engineers will also access generative information, providing insight on performance, wider operations and information which will keep them safe.
The move to self-maintaining networks reduces leaks and water waste, which also improves network efficiency and lowers the pressure on infrastructure. This will benefit the environment, reducing harmful emissions and demands on the freshwater supply. Customers will also have continuous access to their water supply with no disruption.
Beyond using AI in the water network itself, it can also be leveraged for climate risk prediction and disaster response. Through using satellite imagery of water patterns and historical data, AI systems can predict extreme weather events. This will enable water companies to prepare their networks and customers ahead of forecasted natural disasters to ensure a resilient water supply.
From predictive analytics to digital twins, AI is revolutionising how water organisations manage their operations creating an industry that is environmentally friendly and sustainable. The Safe Smart Systems project is reimagining and proving how AI can be used to optimise how resources are utilised across the water industry and wider supply chain. We now have the opportunity to build a water network which is efficient and resilient now and in the future by leveraging AI and data driven solutions.
