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of pipe burst events in water distribution systems. J Hydroinf 15(3):634–651
35. Nguyen KA, Stewart RA, Zhang H (2013) An intelligent pattern recognition model to automate
the categorisation of residential water end-use events. J Environ Modell Software 47:108–127
36. Candelieri A, Soldi D, Archetti F (2015) Short-term forecasting of hourly water consumption
by using automatic metering readers data. Proceedings of CCWI 2015, Leicester, UK, 2nd–4th
September
37. Stewart RA, Nguyen K, Beal C, Zhang H, Sahin O, Bertone E, Vieira AS (2018) Integrated
intelligent water-energy metering systems and informatics: visioning a digital multi-utility
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2018.03.006
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management system: linking smart metering to the future of urban water planning. Aust Planner
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demand pattern, a software approach. Procedia Eng. 89:982–989
40. Rani S, Sikka G (2012) Recent techniques of clustering of time series data: a survey.
Int J Comput Appl 52(15):1–9
Data Science Trends and Opportunities for Smart Water Utilities
25
9:2579–2605
20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
21. Mounce SR (2017) Visualising smart water meter dataset clustering with parametric
t-distributed stochastic neighbour embedding. Proceedings of 13th international conference
on natural computation, fuzzy systems and knowledge discovery (IEEE). Guilin, China.
vol 1, pp. 1940–1945. ISBN: 978-1-5386-2165-3
22. UKWIR (2013) Cost Benefit Analysis of Ubiquitous Data Collection in Water Distribution –
CBA Scenarios. 13/DW/12/2 – ISBN: 1 84057 692 8
23. World Economic Forum (2017). http://reports.weforum.org/digital-transformation/introducingthe-digital-transformation-initiative/
24. Nakamoto S (2009) Bitcoin: a peer-to-peer electronic cash system. Cryptography Mailing list
at. https://metzdowd.com.
25. IWA (Source) (2018) How Bitcoin’s footprint is impacting water use. https://www.
thesourcemagazine.org/how-bitcoins-footprint-is-impacting-water-use/. Accessed 18 Mar 2019
26. Bull R, Azennoud M (2016) Smart citizens for smart cities: participating in the future. Proc Inst
Civil Eng-Energy 169(3):93–101. https://doi.org/10.1680/jener.15.00030
27. Cahn A (2014) An overview of smart water networks. J Am Water Works Ass 106(7):68–74
28. Mounce SR, Mounce RB, Jackson T, Austin J, Boxall JB (2014) Associative neural
networks for pattern matching and novelty detection in water distribution system time series
data. IWA J Hydroinf 16(3):617–632
29. Wu Y, Liu S (2017) A review of data-driven approaches for burst detection in water distribution
systems. Urban Water J 14(9):972–983. https://doi.org/10.1080/1573062X.2017.1279191
30. Mounce, S. R. and Boxall, J. B., (2011). Chapter 9: online monitoring and detection, water
loss reduction. Bentley Institute Press, Exton Ed. Zheng Wu, ISBN: 978-1-934493-08-3.
31. Mounce SR, Fargus A, Weeks M, Young J, Jackson T, Goya E, Boxall JB (2017) Online
advanced uncertain reasoning architecture with binomial event discriminator system for novelty
detection in smart water networks, Proceedings of computing and control for the water industry
(CCWI2017), Sheffield, UK, 5th to 7th September 2017
32. Savić D, Vamvakeridou-Lyroudia L, Kapelan Z (2014) Smart meters, smart water, smart
societies: the iWIDGET project. Procedia Eng 89:1105–1112
33. Farley B, Mounce SR, Boxall JB (2013) Development and field validation of a burst
localization methodology. ASCE J Water Resour Plann Manage 139(6):604–613
34. Romano M, Kapelan Z, Savic DA (2013) Geostatistical techniques for approximate location
of pipe burst events in water distribution systems. J Hydroinf 15(3):634–651
35. Nguyen KA, Stewart RA, Zhang H (2013) An intelligent pattern recognition model to automate
the categorisation of residential water end-use events. J Environ Modell Software 47:108–127
36. Candelieri A, Soldi D, Archetti F (2015) Short-term forecasting of hourly water consumption
by using automatic metering readers data. Proceedings of CCWI 2015, Leicester, UK, 2nd–4th
September
37. Stewart RA, Nguyen K, Beal C, Zhang H, Sahin O, Bertone E, Vieira AS (2018) Integrated
intelligent water-energy metering systems and informatics: visioning a digital multi-utility
service provider. Environ Model Software 105:94–117. https://doi.org/10.1016/j.envsoft.
2018.03.006
38. Stewart RA, Willis R, Giurco D, Panuwatwanich K, Capati G (2010) Web-based knowledge
management system: linking smart metering to the future of urban water planning. Aust Planner
47:66e74
39. Vitorinoa D, Loureirob D, Alegreb H, Coelhob S, Mamadeb A (2014) In defense of the
demand pattern, a software approach. Procedia Eng. 89:982–989
40. Rani S, Sikka G (2012) Recent techniques of clustering of time series data: a survey.
Int J Comput Appl 52(15):1–9
Data Science Trends and Opportunities for Smart Water Utilities
25