AI for Water Utilities: Beyond the Hype to Real Results

Discover how AI transforms water utility operations through predictive models, machine learning, and generative AI. Learn why 90% of utilities fail without proper data infrastructure, and the three-component framework that makes AI implementation successful for leak detection, pipeline management, and treatment optimization.

AI seems to be taking the world by storm but how can it be used in the water utility world? The main types are as follows:

Three Types of AI Transforming Water Operations

Predictive Models: Engineering Meets Intelligence

AI predictive models are decision-making tools that help identify patterns, trends, and relationships in their data. They take historical data add in engineering formulas and known physical relationships to predict different scenarios. This can be useful for planning out a new water plant and you need to estimate the capacity for now and 50 years from now. Or as shown in this image can be used to predict future climate scenarios.

Quick note, regardless of political views, it’s good engineering to look at the most extreme cases of weather, both cooler and warmer when designing long term infrastructure. Otherwise, you end up with issues like the Colorado River basin with professionals estimating the rain would continue forever…and it didn’t.

Machine Learning: Pattern Recognition at Scale

Machine learning is the ability of machines to learn and improve from experience without being explicitly programmed to do so. This means they can take dozens of parameters without explicit engineering relationships and make sense of it. An example below is from pipeline risk AI code that take into account pipe age, pressure, historical breaks, materials, soil types, and other parameters to determine the likelihood of failure. This brings the success rate from about 70% up to 90% in predicting which pipelines will break.

Generative AI: Natural Language for Customer Service

This approach uses deep learning techniques to generate natural language text that is similar to human writing. This took the world by storm in 2023 with the release of Chat-GPT. This takes into large volumes of information and maps it out in a virtual computer mind similar to a human. So, when questions are asked it can respond with speech as if talking to another human. This is already in use in the water industry for customer support roles as shown below.

The Critical Missing Piece: Data Readiness

So, what’s the catch? Why isn’t every utility already using these approaches?

The catch is the data preparedness. Every one of these applications requires good data. Yes, you can use modern AI to predict your pipeline breaks, optimize your treatment plant, and have a human chat style interface. Well, at least you can once you have data.

If you skip the data step what happens? You have some initial success, but the software falls apart quickly. This is why a connected approach of hardware, software, and services for any new technology is required.

The Three-Component Framework for AI Success

Imagine a utility with out-of-control water loss. They want to use the best technology and AI to reduce their losses. Because they have an assortment of sensors, good data across multiple software, and solid services to support they can predict, validate, and repair leaks.

Success requires three integrated components:

Hardware Infrastructure

Pressure sensors throughout the distribution network
Flow meters at critical junctions
Acoustic leak detection equipment
Smart meters for consumption monitoring

Software Systems

SCADA for real-time monitoring
Work order management tracking repairs and breaks
GIS mapping infrastructure assets
Analytics platforms processing sensor data
AI models identifying patterns and anomalies

Professional Services

Field crews performing leak surveys
Technicians repairing identified issues
Engineers analyzing system performance
Data specialists maintaining quality inputs

If one piece in this chain breaks down that makes the whole process much harder. If there are no pressure sensors in the system, it will be difficult to monitor pressure spikes in the system which are one of the top causes of pipeline fatigue and breaks. Without a work order tracking software in place there will be no historical pipeline breaks and repairs to use as training data for the pipeline failure AI model. Without services to repair the identified leaks then the leaks will stay just that…leaky.

Moving Beyond Single-Solution Promises

Each of these components work together, be wary of anyone that promises to solve your utility issues with just a single approach. A utility requires all three!