Data Science Trends and Opportunities
for Smart Water Utilities
Stephen R. Mounce
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1 Data Rich Information Poor: Fixing the DRIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Big Data and Analytics Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Hydroinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Data Analytics: Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Data Analytics: Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1 Internet of Things and Edge Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 Cloud Computing and Condition Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5 Digitalisation . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . 12
6 Blockchain, Data Sharing and Web 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7 Smart Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8 Use of Smart Meter Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9 Discussion of Technology Adoption and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Abstract We are witnessing an industry change which is transitioning to a more
intelligent (or smarter) water network. In the UK a 5-year planning period
and investment cycle called the Asset Management Plan (AMP) is the regulatory
mechanism. This process is used to manage a water utility’s infrastructure and
other assets to deliver an agreed standard of service. The challenge of AMP
6 and 7 (to 2025) and beyond is to maximise efficiency by moving from reactive
to proactive management. This can be achieved by using data, information and
(where possible) control of the system. The more intelligence that is captured,
the more that can be learned and understood about the network and subsequently
be predicted. Extra data provides new opportunities for asset maintenance and
event analytics. Data science is an emerging discipline which combines analysis,
S. R. Mounce (*)
Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
Mounce HydroSmart, Beverley, UK
e-mail: s.r.mounce@sheffield.ac.uk
Andrea Scozzari, Steve Mounce, Dawei Han, Francesco Soldovieri,
and Dimitri Solomatine (eds.), ICT for Smart Water Systems: Measurements and
Data Science, Hdb Env Chem (2021) 102: 1–26, https://doi.org/10.1007/698_2020_482,
© Springer Nature Switzerland AG 2020, Published online: 22 June 2020
1
for Smart Water Utilities
Stephen R. Mounce
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1 Data Rich Information Poor: Fixing the DRIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Big Data and Analytics Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Hydroinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Data Analytics: Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Data Analytics: Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1 Internet of Things and Edge Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 Cloud Computing and Condition Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5 Digitalisation . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . 12
6 Blockchain, Data Sharing and Web 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7 Smart Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8 Use of Smart Meter Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9 Discussion of Technology Adoption and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Abstract We are witnessing an industry change which is transitioning to a more
intelligent (or smarter) water network. In the UK a 5-year planning period
and investment cycle called the Asset Management Plan (AMP) is the regulatory
mechanism. This process is used to manage a water utility’s infrastructure and
other assets to deliver an agreed standard of service. The challenge of AMP
6 and 7 (to 2025) and beyond is to maximise efficiency by moving from reactive
to proactive management. This can be achieved by using data, information and
(where possible) control of the system. The more intelligence that is captured,
the more that can be learned and understood about the network and subsequently
be predicted. Extra data provides new opportunities for asset maintenance and
event analytics. Data science is an emerging discipline which combines analysis,
S. R. Mounce (*)
Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
Mounce HydroSmart, Beverley, UK
e-mail: s.r.mounce@sheffield.ac.uk
Andrea Scozzari, Steve Mounce, Dawei Han, Francesco Soldovieri,
and Dimitri Solomatine (eds.), ICT for Smart Water Systems: Measurements and
Data Science, Hdb Env Chem (2021) 102: 1–26, https://doi.org/10.1007/698_2020_482,
© Springer Nature Switzerland AG 2020, Published online: 22 June 2020
1