{"id":31521,"date":"2018-11-13T13:51:46","date_gmt":"2018-11-13T18:51:46","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/under-pressure-how-machine-learning-can-help-tackle-leakage-in-european-water-distribution\/"},"modified":"2018-11-13T19:18:14","modified_gmt":"2018-11-14T00:18:14","slug":"under-pressure-how-machine-learning-can-help-tackle-leakage-in-european-water-distribution","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/under-pressure-how-machine-learning-can-help-tackle-leakage-in-european-water-distribution\/","title":{"rendered":"Under Pressure: How machine learning can help tackle leakage in European water distribution"},"content":{"rendered":"

While it may seem surprising in an environmentally-conscious world, 3 billion liters of water is leaked into the ground every day in the UK. To put that into context, that amount could meet the daily needs of 20 million UK consumers[1]<\/a><\/sup>.<\/p>\n

 <\/p>\n

Indeed, many of Europe\u2019s water scarce countries (Spain, Italy and the UK, among others) have an endemic \u2018leakage\u2019 problem (see chart below) \u2013 up to 50% of treated water pumped into a \u2018network\u2019 (i.e., system of underground water pipes), is leaked back into the ground[2]<\/a>[3]<\/a><\/sup>.<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

Introducing United Utilities<\/strong><\/p>\n

United Utilities is a publically-listed water and wastewater company serving 3 million households across the North West of England,<\/a><\/sup>\u00a0who themselves leak 430 million liters of water a day<\/a>[4]<\/a>[5]<\/sup>. According to their regulatory disclosures, between 2020-2025 they plan to spend \u00a31 billion on maintaining and improving their water network, with a significant focus on leakage reduction[6]<\/a>[7]<\/a><\/sup>.<\/p>\n

 <\/p>\n

Process Improvement Potential<\/strong><\/p>\n

Effective leak identification is an extremely important process improvement lever for United Utilities. First, they face severe regulatory scrutiny in this area. Thames Water, a competitor, was fined \u00a3120 million last year for failing to hit its leakage targets[8]<\/a><\/sup>. Second, there are also significant sunk costs related to leakage; namely, the cost of treating and pumping water before it leaks, alongside the labor costs associated with locating leaks[9]<\/a><\/sup>.<\/p>\n

 <\/p>\n

Leaks can take two forms; overground or underground. Overground leaks are easier to identify, as they are typically reported by customers. For underground leaks, water companies have historically used a field force with acoustic equipment who \u2018listen\u2019 to detect anomalies in the flow of a pipe – but such methods are inefficient and costly[10]<\/a><\/sup>. United Utilities have also tried other solutions; including training drug-sniffing dogs to detect leaks[11]<\/a><\/sup>!<\/p>\n

 <\/p>\n

Over the past years, much has been written about the application of machine learning to leak detection. By using historic data related to the flow and pressure levels of water pipes, coupled with the location of previous leaks, a machine can be trained to detect the anomalies that accompany, or foreshadow, leaks[12]<\/a><\/sup>. United Utilities is one of the early adopters of these methods.<\/p>\n

 <\/p>\n

Machine Learning at United Utilities<\/strong><\/p>\n

In 2017, United Utilities first utilized machine learning with their Event Recognition in Water Network (ERWAN) tool. By teaching the tool based on ~200 million network data-points, it develops a \u2018baseline\u2019 for normal operation. When ERWAN detects a deviation from this baseline, it sends a simple alert to operators. In select cases, this has allowed a reduction in response times to overground leaks by ~40%. This technology will be fully embedded over the coming years[13]<\/a><\/sup>.<\/p>\n

 <\/p>\n

For the longer-term, United Utilities have partnered with Emagin to develop a more sophisticated machine learning tool; Hybrid Adaptive Real-Time Intelligence (HARVI). HARVI learns off a wider assortment of data, including weather and electricity data, and runs simulations to suggest an optimal system operating mode. To give an example, if there is a burst it will suggest rerouting water to avoid excessive leakage in this area. Initial results appear promising, with a ~22% cost saving in pilot areas. Regional rollout is expected throughout the 2020-2025 period[14]<\/a><\/sup>.<\/p>\n

 <\/p>\n

Recommendations<\/strong><\/p>\n

In my view, United Utilities management should focus on two areas to build off their initial machine learning success.<\/p>\n

 <\/p>\n

First, they must maximize data collection across their network. United Utilities have 42,000 km of water pipes across 3,000 sub-regions[15]<\/a><\/sup>. To provide full coverage of potential leak locations, it is estimated they need ~20 sensors per sub-region area, implying they need ~60,000 network sensors[16]<\/a><\/sup>. While United Utilities do not publically disclose their current number of sensors, Thames Water have 26,000 sensors\/loggers across their water network[17]<\/a><\/sup>. Assuming a similar number for United Utilities, given their comparable size, would imply less than 50% coverage. Investment to improve data coverage will allow a machine to accurately pinpoint leak locations across the network.<\/p>\n

 <\/p>\n

Second, they should invest to develop truly predictive tools. ERWAN and HARVI are reactive in nature \u2013 they respond to leakage events and try to minimize the impact. I believe the best application of machine learning is in predicting where large leaks will occur. For example, with the right data and machine in place, a probability of a major leak can be assigned to each section of pipe. From there, proactive maintenance can be completed on high-risk sections. While this level of sophistication will take time to develop, this should be the ultimate goal of management.<\/p>\n

 <\/p>\n

Outstanding Questions<\/strong><\/p>\n

A number of questions remain open for me on this topic; 1) Given that this industry is not a typical destination for machine learning expertise, how can United Utilities attract top talent? 2) How can governments incentivize sharing of technology in competitive markets like this, given the significant societal benefits to doing so? [799 Words]<\/em><\/p>\n

 <\/p>\n

\n

<\/a>[1]<\/sup>\u00a0Henry Bodkin, \u201cBritain to face widespread drought by 2050 unless leaky pipes fixed,\u201d The Telegraph<\/em>, May 2018, https:\/\/www.telegraph.co.uk\/news\/2018\/05\/22\/water-firms-told-leak-less-officials-warn-widespread-drought\/<\/a>, accessed November 2018.<\/p>\n

<\/a>[2]<\/sup>\u00a0European Environment Agency, \u201cWater scarcity,\u201d November 2018, https:\/\/www.eea.europa.eu\/archived\/archived-content-water-topic\/featured-articles\/water-scarcity<\/a>, accessed November 2018.<\/p>\n

<\/a>[3]<\/sup>\u00a0EurEau \u2013 European Federation of National Associations of Water Services, \u201cEurope\u2019s water in figures,\u201d October 2017, http:\/\/<\/a>www.eureau.org\/resources\/publications\/1460-eureau-data-report-2017-1\/file<\/a>, accessed November 2018.<\/p>\n

<\/a>[4]<\/sup>\u00a0United Utilities, \u201cAbout Us,\u201d https:\/\/www.unitedutilities.com\/about-us, accessed November 2018.<\/p>\n

<\/a>[5]<\/sup>\u00a0BBC News, \u201cWater chiefs must \u2018explain leakage target failures\u2019,\u201d July 2018, https:\/\/www.bbc.com\/news\/uk-44996648<\/a>, accessed November 2018.<\/p>\n

<\/a>[6]<\/sup>\u00a0United Utilities, \u201cPR19 Customer Facing Document,\u201d September 2018, https:\/\/www.unitedutilities.com\/globalassets\/z_corporate-site\/pr19\/c0008_pr19_customer_facing_document.pdf<\/a>, accessed November 2018.<\/p>\n

<\/a>[7]<\/sup>\u00a0United Utilities, \u201cUnited Utilities PR19 Business Plan Submission,\u201d September 2018, https:\/\/www.unitedutilities.com\/globalassets\/z_corporate-site\/financial-news-2018\/united-utilities-proposed-business-plan-submission.pdf<\/a>, accessed November 2018. Note<\/u>: \u00a31 billion calculated by using network proportion of daily bill (19c\/105c), multiplied by total planned spend of \u00a35.4 billion.<\/p>\n

<\/a>[8]\u00a0<\/sup>Gill Plimmer, \u201cThames Water told to repay users \u00a3120m for failing to plug leaks,\u201d Financial Times<\/em>, June 2018, https:\/\/www.ft.com\/content\/e1257fc4-601a-11e8-ad91-e01af256df68<\/a>, accessed November 2018.<\/p>\n

<\/a>[9]<\/sup>\u00a0S. R. Mounce, C. Pedraza, T. Jackson, P. Linford, J. B. Boxall, \u201cCloud based machine learning approaches for leakage assessment and management in smart water networks,\u201d in 13th International Conference on Computing and Control for the Water Industry, <\/em>Leicester UK, 2015, pp. 43-52.<\/p>\n

<\/a>[10]<\/sup>\u00a0G. Kunkel and R. Sturm, \u201cPiloting proactive, advanced leakage management technologies,\u201d American Water Works Association<\/em>, 103(2) (2011): 62-75.<\/p>\n

<\/a>[11]<\/sup>\u00a0Telegraph Reports, \u201cBritain\u2019s first water sniffing dog hired to pinpoint leaks and broken pipes\u201d, February 2018, https:\/\/www.telegraph.co.uk\/news\/2018\/02\/11\/britains-first-water-sniffing-dog-hired-pinpoint-leaks-broken\/<\/a>, accessed November 2018.<\/p>\n

<\/a>[12]<\/sup>\u00a0D. Varies, B. van den Akker, E. Vonk, W. de Jong, J. van Summeren, \u201cApplication of machine learning techniques to predict anomalies in water supply networks,\u201d Water Science & Technology: Water Supply<\/em>, 16(6) (2016): 1528-1535.<\/p>\n

<\/a>[13]<\/sup>\u00a0United Utilities, \u201cCapital Markets Event,\u201d March 2018, https:\/\/www.unitedutilities.com\/globalassets\/z_corporate-site\/financial-news-2018\/united-utilities-capital-markets-event-presentation-march-2018.pdf<\/a>, accessed November 2018.<\/p>\n

<\/a>[14]<\/sup>\u00a0United Utilities, \u201cCapital Markets Event,\u201d September 2018, https:\/\/www.unitedutilities.com\/globalassets\/z_corporate-site\/financial-news-2018\/capital-markets-day-presentation-final.pdf<\/a>, accessed November 2018.<\/p>\n

<\/a>[15]<\/sup>\u00a0Bloomberg, \u201cCompany Overview of United Utilities Group PLC,\u201d https:\/\/www.bloomberg.com\/research\/\/\/stocks\/private\/snapshot.asp?privcapId=408730<\/a>, accessed November 2018.<\/p>\n

<\/a>[16]<\/sup>\u00a0David A. Lloyd Owen, Smart Water Technologies and Techniques: Data Capture and Analysis for Sustainable Water Management<\/em> (Hoboken, NJ: John Wiley & Sons, 2018), pp. 144-145.<\/p>\n

<\/a>[17]<\/sup>\u00a0Thames Water, \u201cOur leakage performance,\u201d\u00a0https:\/\/www.thameswater.co.uk\/help-and-advice\/leaks\/our-leakage-performance<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

What if I told you 3 billion liters of water is leaked into the ground every day in the UK? 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