Debra Bruno is a writer in Washington. Her last piece for Politico Magazine took a look at the nation’s obsession with costly street car projects.

SYRACUSE, N.Y.—It was a nightmare scenario: As thousands of Syracuse University basketball fans poured into town on February 1, 2014 for a big match against arch rival Duke, a water main break flooded Armory Square, surrounding the city’s iconic 24-second shot clock monument. Days before the game, there were 11 other water main breaks around the city.

Mayor Stephanie Miner was desperate for help to get a handle on the problem; on average, water lines in the city were breaking 332 times a year, nearly once every day. But she couldn’t get the state to help foot the bill for the onerous costs of updating the city’s underground infrastructure. She even tried to shame state officials with a “Hunger Games”-style ad campaign that showed her wading in thigh-high water wielding a wrench. Miner says that when she started asking federal and state officials for help, she got a lot of eye rolling. “They would say, ‘Stephanie, you can’t cut a ribbon with it. Stephanie, it’s not sexy,’” she says. She had to get creative.

That’s when she turned to big data. To get to the bottom of the problem of catastrophic water main breaks, Syracuse first had to understand what was happening underground and where. Using an algorithm developed by a team at the University of Chicago, the city put reams of information, scattered among various departments, to work. With a predictive system that can point to the hotspots along its 550 miles of pipes, the city hopes to save millions of dollars a year by fixing mains before they break. For other cities dealing with the same whack-a-mole approach to infrastructure repair, a proactive approach could change everything.

The American Society of Civil Engineers, in its 2017 Infrastructure Report Card, estimated that the U.S. endures 240,000 water main breaks a year on more than 1 million miles of pipes, many of them laid in the early to middle years of the 20th century. If cities keep up their average of replacing pipes at 0.5 percent a year, it would take about 200 years to replace them all. If they last that long, of course.

Other cities could use Syracuse’s big data approach to anticipate their own water delivery problems. Flint, Michigan, for example, which is facing a few more issues than water main breaks, is hoping that as it replaces its old damaged lines a good data management approach will help prevent other nightmares.

“Where they are in Syracuse is the direction we want to go,” says Mayor Karen Weaver. Syracuse sent its innovation team to Flint, where they looked at the city’s mapping system and records of water main breaks and realized that the Syracuse formula could be useful there, too. Sam Edelstein, chief data officer for Syracuse, says, “The hope is that the solutions that we come up with are scalable and can be used elsewhere.”



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Syracuse is a city that cares about water. Syracuse is one of only two cities in the state—the other is New York City—that has such a clean water supply that it does not need a filtration plant. Syracuse water comes in a gravity-fed line from Skaneateles Lake, a Finger Lake about 30 miles southwest of the city, and is considered by some to be one of the cleanest lakes in the U.S. Miner’s press secretary Alexander Marion notes that newcomers are offered a glass of “Skaneateles on the rocks”—tap water, in other words.

A quick reality check, though: Syracuse is also adjacent to Lake Onondaga, which the New York State Department of Energy and Conservation has named the “most polluted lake in America,” thanks to industrial waste related to the city’s salt-mining history and years of untreated sewage dumping.

In short, Syracuse is an aging industrial city with a dwindling population, a crime problem, and bitter cold winters typical of upstate New York. It averages nearly one water main break a day, which is not unusual for an older northern city but which costs the city $1 million a year for repairs and replacement. Its water main system has parts that are more than 100 years old. Miner, the first woman to serve as mayor in the city’s 169-year history, has been known to call in water main breaks from her car, before anyone else has notified the public works department.

A cyclist rides along Onondaga Lake Park in Liverpool, N.Y. | AP Images

When Miner took office in 2010, the city started experiencing a surge of breaks—338 before the end of the year, almost 100 more than the previous year. “It just happened to be my luck,” she says, combined with deferred maintenance on a system that was, as she puts it, “old and cold.”

Miner, 46, called out Governor Andrew Cuomo in 2015 over funding. She wanted the state to use $800 million of its legal and financial settlement money to fix sewer and water systems, rather than focus on the economic development Cuomo touted. (That’s when she deployed the Hunger Games parody.) Miner’s logic was simple: “Why would we spend millions of dollars on economic development above a system and then not pay any attention below and a month later have a road blow up because we didn’t replace the water mains?”

The bigger question was, what would be the smartest way to spend the city’s limited resources? The search for an answer was helped along in 2015 by a three-year grant of $1.35 million from Bloomberg Philanthropies to create the City of Syracuse’s i-team, which is focused on infrastructure improvement. Using the know-how of a team from the University of Chicago’s Eric and Wendy Schmidt Data Science for Social Good program, Syracuse began a laborious project to first gather and enter the data into a digital form, and then create an algorithm that would predict just where those mains were most likely to break.

This machine-learning system, an application of artificial intelligence, homed in on 50 (out of 5,263) of the city’s most break-prone blocks and pointed to 32 blocks that were most likely to break in the next three years.

To get to that formula, researchers applied a series of factors—age of pipes, construction material, previous breaks and pipe dimensions—to breaks that happened in the past as a way to “predict the past,” or test whether the formula, working blind, could accurately guess which mains would break. Rayid Ghani, director of the University of Chicago’s Data Science for Social Good summer fellowship, says, “If you have 10 years of data, you take nine years and hide the tenth year from the system. So you pretend it’s 2015 and you try to predict what would have happened.”

One surprise in the findings, notes Ghani, was that pipes that had broken recently tended to be more likely to break again, possibly because of some intrinsic flaw that hadn’t been corrected with a repair. Keep in mind that the city expects to see 500 to 600 breaks over the next few years, says data officer Edelstein. When the city does replace some mains in the 32 hotspots, “we’d be pretty sure we are replacing the ones most likely to break,” he says.

One of the more complicated tasks was collecting the data to begin with. Syracuse had a hodgepodge of records that included 100-year-old field notebooks hand-written by engineers, Excel spreadsheets, items in a Word document on one person’s desktop computer, and rolls of paper piled up in a closet, says Andrew Maxwell, head of the innovation team that ran the project. That information was added to a geographic information system mapping city streets, property tax reports and records of past breaks—all of it then funneled through an algorithm that made predictions about the next break.



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In the year and three months since the test run began, there have been 21 breaks on 14 mains (some broke more than once) in the targeted spots, says Edelstein. In other words, “we’re right about on pace” with predictions, he says.

A Syracuse Water Department contractor attaches a blue sleeve connector to a broken water main. In 2014 the city had nearly 400 water main breaks. | AP Images

Eventually, Syracuse also wants to incorporate a sensor system, which uses acoustic waves on monitors magnetically attached to pipes and joints to detect where leaks are happening, how big they are and whether they warrant immediate repair. “The idea is to fix it before it becomes catastrophic,” says Miner. Using sensors in this way is not new, but feeding that data into a larger predictive model would be.

Yet another aspect of the plan is Syracuse’s “dig once” policy. “If we dig into the ground,” says Miner, “we want to be able to replace the sewer mains, the water mains, the utility lines, and hopefully broadband—and do it all at once.”

The city is also using data to determine which of its roads are in bad shape, which it will coordinate with the water main information. “You take those maps and you overlay them with the water maps. All of a sudden, you’re looking at infrastructure in three dimensions,” says Miner. Doing that in two pilot projects last year saved the city almost $500,000, she says.

At this point, however, most of Syracuse’s ground plan is still in the theory and testing phase. A more extensive proposal has been added to the city’s 10-year capital plan.

Eventually, other cities might be able to plug in their own data and create predictive systems. The code for the Syracuse project is available as open source software on Github.

While it will be many years—if ever—before water main breaks are not a nasty surprise that shut down businesses and disrupt lives, a data solution could be a step toward being proactive rather than reactive. Avishek Kumar, a member of the data science team at the University of Chicago, notes that Syracuse is not the only city facing devastating main breaks. “We should be able to solve this problem for any city.”