AI-controlled water distribution network, visualizing smart water management.

Smart Water: How AI Could Be Fixing Leaks Before They Start

Mira Elwood in Tech & Innovation August 2025 • 4 min read.

"AI-powered iterative learning control offers a new approach to managing water pressure, minimizing leaks, and conserving resources in distribution networks."

Water scarcity is a growing global concern, exacerbated by significant water loss due to leaks in distribution networks. Traditional methods of pressure management have proven to be effective in reducing leaks and bursts, but a new approach is needed to integrate advanced technologies for smart and efficient water resource management. With pressure too low, end-users don't get the water they expect, in turn leading to pollutants entering the network.

Research into water distribution systems has largely focused on optimal pump scheduling, leakage detection, and contamination prevention. One promising area is the use of iterative learning control (ILC), a technique that leverages past data to refine control actions and improve system performance over time. By applying ILC to pressure control, we can meet pressure requirements at critical points in the network while minimizing unnecessary pressure levels.

Iterative Learning Control (ILC) offers a dynamic approach to refining control inputs. This technique is beneficial for managing systems such as mechanical robots or production lines and is now being applied to enhance smart pumping station control. Iterative learning uses logged pressure data to calculate the most efficient pressure, reducing reliance on internal models. It's particularly effective because water usage often follows predictable patterns.

The AI Model: Reducing Water Loss

AI-controlled water distribution network, visualizing smart water management.

Water distribution networks can be modeled as a graph, with vertices representing pipe connections and edges representing the pipes themselves. Each vertex is associated with pressure, demand, and geodesic level, while each edge is characterized by pressure drop due to hydraulic resistance. This model allows us to analyze the network's behavior and develop control strategies to optimize pressure levels.

To simplify the analysis and control design, we can create a reduced-order model by partitioning the network into inlet vertices (where water enters the network) and non-inlet vertices (representing end-users). By making certain assumptions about the network, such as uniform head at all inlets and consistent consumption profiles at non-inlet vertices, we can derive a simplified expression for pressure at each non-inlet vertex. This expression relates pressure to total demand, inlet pressure, and a constant term that captures the network's physical characteristics.

Reduced Leaks: By maintaining optimal pressure, the likelihood of leaks due to excessive pressure is significantly reduced.
Energy Savings: Lowering the amount of energy used by pumps.
Improved Comfort: Stable pressure for all consumers.
Avoiding increased risks of pollutants.

The control objective is to maintain a minimum pressure requirement at the measured vertices. The iterative learning control (ILC) algorithm adjusts the inlet pressures based on past performance to achieve this objective. By iteratively refining the control actions, the system learns to compensate for disturbances and uncertainties, ensuring that pressure requirements are met while minimizing unnecessary pressure levels. The controller will continuously adjust the inlet pressure to reduce pipe stress and overall energy consumption. Furthermore, the proposed control gives pressure set points to all inlets in the network instead of flow set-points thus reducing the need for flow measurements which are typically more expensive.

The Future of Smart Water Networks

The ILC-type control structure offers a promising approach to pressure control in water distribution networks, enabling reduced leaks, energy savings, and improved system performance. By leveraging AI and machine learning, we can create smarter and more resilient water systems that are better equipped to meet the challenges of water scarcity and environmental sustainability. Future research should focus on estimating inlet node elevation, handling sensor dropouts, and extending the approach to networks with elevated storage to push the boundaries of innovation in smart water management.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/ccta.2018.8511513, Alternate LINK

Title: Iterative Learning Pressure Control In Water Distribution Networks

Journal: 2018 IEEE Conference on Control Technology and Applications (CCTA)

Publisher: IEEE

Authors: Tom Norgaard Jensen, Carsten Skovmose Kallesoe, Jan Dimon Bendtsen, Rafal Wisniewski

Published: 2018-08-01

Everything You Need To Know

How does Iterative Learning Control (ILC) use data to optimize pressure in water distribution networks?

Iterative Learning Control (ILC) uses past data to refine control actions. In the context of water distribution networks, logged pressure data is analyzed to calculate the most efficient pressure, reducing reliance on complex internal models. This is particularly effective because water usage follows predictable patterns. By iteratively adjusting the inlet pressures based on past performance, the system compensates for disturbances, minimizes unnecessary pressure levels, and maintains minimum pressure requirements at measured vertices.

What is the main objective of using Iterative Learning Control (ILC) in managing water pressure, and what benefits does it offer?

The primary goal of using Iterative Learning Control (ILC) in water distribution networks is to maintain a minimum pressure requirement at critical points while minimizing unnecessary pressure levels. The Iterative Learning Control (ILC) algorithm adjusts the inlet pressures based on past performance to achieve this objective, reducing leaks, optimizing energy consumption, and ensuring stable pressure for all consumers. This approach also helps in avoiding the increased risks of pollutants entering the system due to low pressure.

How is a water distribution network modeled for analysis, and what do the different components represent?

In a water distribution network, the network is modeled as a graph. Vertices represent pipe connections, each associated with pressure, demand, and geodesic level. Edges represent the pipes, characterized by pressure drop due to hydraulic resistance. This model helps analyze the network's behavior and develop control strategies to optimize pressure levels. For simplification, the network can be partitioned into inlet vertices (where water enters) and non-inlet vertices (representing end-users), enabling a simplified expression for pressure at each non-inlet vertex related to total demand, inlet pressure, and a constant term reflecting the network's physical characteristics.

What are the limitations of traditional methods in managing water pressure, and how does Iterative Learning Control (ILC) address these?

Iterative Learning Control (ILC) addresses the limitations of traditional methods by leveraging past data to refine control actions, improving system performance over time. Traditional methods primarily focus on pump scheduling, leakage detection, and contamination prevention, whereas Iterative Learning Control (ILC) offers a dynamic approach to refining control inputs, enabling smart pumping station control and enhancing overall water resource management by reducing leaks and bursts through optimized pressure management.

What are the next steps in enhancing Iterative Learning Control (ILC) for smart water networks, and what impact will these advancements have?

Future research should focus on estimating inlet node elevation, handling sensor dropouts, and extending the approach to networks with elevated storage. These advancements aim to push the boundaries of innovation in smart water management, making water systems more resilient and better equipped to handle water scarcity and environmental sustainability. By addressing these areas, Iterative Learning Control (ILC) can be further optimized for broader and more complex water distribution networks.