The path forward: smarter, data-rich water management

Water management technologies have evolved dramatically since the days when listening sticks were about the only leak detection device available to utilities. And today, water management is about far more than leak detection. In fact, it’s no longer even about just water management; now it’s increasingly about data technologies and smart water management.

So, why is smart water management needed? And where is it going from here?

First steps toward smart water management
Utilities have long employed a variety of approaches to reduce water loss. Devices like simple listening sticks have been around for decades, as have billboards and radio advertisements urging consumers to call the water utility when they notice leaks or other problems. Periodic leak detection campaigns, in which teams scan different sections of the network on a preset schedule, is another common approach.

Those methods might have sufficed in the past, but today, many utilities understand that they need more advanced techniques that give them better visibility into what is happening in their underground network. In addition to reducing non-revenue water (NRW), they are looking to new technologies and processes to help them improve their operational efficiency and customer service.

Getting data is easy; getting insights from it is not
To this end, forward-thinking utilities are starting to establish district metered areas (DMA) and install digital water devices that generate data about conditions in the network, such as flow and pressure meters, fixed and mobile acoustic loggers, pressure transient sensors, water quality sensors, and pressure reducing valves (PRV).

Installing digital water devices is the first step in evolving toward smart water management. No utility should depend on just one type of technology, but rather each utility should combine multiple technologies according to the specific needs of the network. A utility in a densely built-up urban area (where fixing a leak could be challenging and expensive) might use methods for accurate leak detection and smaller DMAs, while a utility in a more rural area that covers a wider geographic area will get more value from pressure control and larger DMAs. Of course, environmental factors will make a huge difference too. Networks in hot and dry climates will likely need different approaches than those in colder and wetter regions.

The data generated by the various devices and systems can be processed and analyzed to extract insights about network conditions, leaks and other sources of NRW that can guide proactive repairs and maintenance. But the huge amount of data that is generated makes the ‘understanding’ process very complex – the more devices and systems, the more data, and the more difficult it is to extract insights. Therefore, advanced analytics tools and methods are needed to identify trends and generate visibility.

The missing piece: smart integration
While the various digital water systems typically offer some analytics, each one employs its own methodology tailored for the specific type of data. As a result, insights about different aspects of the network are siloed in different systems.

When there is one system for flow or customer usage data, another screen for acoustic logger data and yet another for satellite detection, it’s impossible to get a single comprehensive representation of the network status. It still takes multiple manual steps to detect leaks and other incidents, and to prioritize repair and maintenance activities – a process that is time-consuming and prone to error and inconsistencies. The siloed insights and convoluted processes also make it extremely difficult to dynamically re-prioritize activities as situations change. And, as more sensors and other devices are added, it becomes harder and harder to handle the huge and ever-increasing volumes of data generated across the network.

This brings us to the final crucial step utilities need to gain the effective visibility and control of their network that they are aiming for: integration. It’s vital to bring together the insights from the multiple different technologies and systems into a single platform where they can all be viewed together via one dashboard, one point of truth.

In short, integration of data as well as of the insights from multiple sources and sy

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Taxonomy

• Data Management
• Remote Sensing & Data Analysis
• Data Processing
• Data Acquisition
• Data & Analysis
• Data Science
• Water Treatment Enterprise Information Data Management
• Data Analysis
• Data Visualisation