Few environmental limits are as obvious to people today as water availability. Particularly in drier climates, availability can be a pretty unforgiving equation. Even there, a family might pay less for water than for cell phones, but there is often a pretty complex system behind your tap that keeps it running.

The challenge of water availability rises beyond engineering. It becomes a delicate dance managing demand, forecasting supply, and sustaining ecosystems. Decisions have to be made based on information that is never complete, so any opportunity to obtain more useful information is liable to get a thirsty look from water managers.

Of course, a truck load of information won’t do you any good if you can’t extract the bits you need. One tool for working with potentially valuable truck loads is an artificial neural network—a software system that uses machine learning techniques to process tons of data and intelligently answer questions. And one company is now applying IBM’s Watson machine learning system in an interesting way to tell water utilities something they would love to know: how efficiently their customers are using water.

The beauty of machine learning is that you don’t need to craft sophisticated algorithms to get it to work. For example, to identify certain things in images, you feed the system a pile of examples and it figures it out itself. Given enough images known to contain an apple, for example, it will reliably spot apples in the future—and it can often churn through thousands of images incredibly quickly to do so. It’s less like simple pattern matching and more like human inference. Given observations x, y, and z, how likely is it that this object is an apple?

Watson does water

A geographical analytics company called OmniEarth has Watson churning through banks of aerial and satellite photos to estimate the demand for water on a property-by-property level based on what the property contains. “Our service itself has never really been trained for aerial imagery. It’s really about a general visual recognition service, but what they found is that it actually worked pretty well when they gave it enough training examples,” IBM’s Jerome Pesenti told Ars. “So then they took hundreds of thousands of images of the whole state of California, passed them through the classifier that they had trained, and they were able to identify all these features.”

OmniEarth Chief Strategy Officer Jonathan Fentzke explained, “What we do is, for every parcel or region of interest, we calculate the square footage of tree and shrub and grass and pool and roof and irrigated and non-irrigated surface.” Along with census numbers and weather and evaporation data, that information is used to estimate the actual demand for water at that parcel.

If you run a water district, that’s a number you’d like to have. Utilities will obviously know how much water each customer actually uses each month, but it’s harder to guess whether much of that usage was wasted. Some big water users may have already cut out all the inefficient stuff and are simply covering their demand. Some small users could be pulling twice as much water as they really need. Without some context, a water meter reading can't tell the difference.

“They can tell you how good, bad, or ugly customers’ water use is, how efficient it is, who’s wasting water, where that is,” said Tom Ash of the Inland Empire Water Utilities Agency, which covers almost a million people east of Los Angeles. “And then [utilities] know who to target with what kind of program. If you don’t have any landscape then you target them for, jeez, maybe you’ve got leaks, maybe you’ve got high-flow plumbing devices that you need to retrofit—great, we’ve got a program. So it really helps make the efficiency level and outreach of your conservation programs much more effective.”

Water districts sometimes run water use surveys to try to learn about their users, but direct measurements with complete coverage would obviously have advantages over survey samples.

Machine learning vs. drought

For the Inland Empire Water Utilities Agency and the municipal districts it works with, accessing OmniEarth’s data was part of the response to the ongoing drought and the 25-percent reduction in water usage mandated by the state of California last year. The only thing a resident would notice out of all this is that any information on water conservation programs they receive is likely to be directly applicable—it’s not like a list of “San Bernardino County’s Most Wanted Water Wasters” went up in local post offices. In some places, individuals may also be able to see the efficiency estimate for their property through connections with water-tracking apps like Dropcountr.

Ash noted that the districts working with this information last year learned something useful for California in general—last year’s across-the-board emergency 25-percent cut to water usage could be achieved on a permanent basis if everyone just met the state’s efficiency standards. “They had to do something quickly because we literally didn’t have any snowpack in 2014-2015. It wasn’t necessarily a fair and equitable allocation of pain across the agencies and up and down the state,” Ash told Ars. “It enabled us to take a look and go, ‘Wow—if everybody just hit these efficiency targets, we wouldn’t have to have these arbitrary and inequitable targets. We could put everybody on the same playing field.’”

At the same time, monitoring changes over time in the satellite and aerial imagery (like the removal of turf, for example) along with changes in water usage could help water districts see the impact of their conservation programs.

For water managers, anything that lets them peer into the black box at the other end of the pipe is a boon. No one that watched the levels of California’s reservoirs in 2015 needs much of a reminder why these managers have an important job.