Abstract
One of the most basic challenges in urban areas is providing sustainable access to adequate quantities of quality water in order to sustain livelihoods, human well-being, and socio-economic development. Water poverty affects an important share of low income urban and rural population in the forms of limited, time-consuming and unsafe access to the resource as well as a high incidence of waterborne diseases. Universalizing access to potable water and sanitation by being efficient and avoiding waste of resources may be the most important challenge of water networks in future years. ‘Smart water’ consists in a group of emerging technological solutions that help water managers operate more efficiently and, in a smaller scale, also help consumers tracking and managing their water usage. The Internet of Things, cloud-based information storage and data analytics (Big Data) are at the core of that. A smart water system is based on a network of sensors embedded with electronics and software that allow getting real-time data of any measurable parameters such as level, flow, pressure, temperature, noise correlations or even water quality parameters, and make them available online. Furthermore, the management of data through statistical tools and algorithms can allow pattern recognition and modeling of the system, thus optimizing the operational performance of the water supply network and reducing pipe bursts, leakages and energy waste in the pumps.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ministry of the Cities of Brazil (2018) Sistema Nacional de Informações sobre Saneamiento. Brasilia
EPA—U.S. Environmental Protection Agency. Control and Mitigation of Drinking Water Losses in Distribution systems. EPA 816-R-10-019, Office of Water, Nov 2010
Lima IM, Silveira ABG (2018) Ratio of real to apparent losses in Brazil. In: B26 Paper. Water loss conference and exhibition—Waterloss 2018, Cape Town, 7–9 May 2018
Zakai A, Bar-Or J (2010) Valve for prevention of low flow rates through flow meter. US Patent 7,640,944
AWWA—American Water Works Association (2016) M36 Water audits and loss control programs, 4th edn, p 442
AWWA—American Water Works Association. AWWA Free Water Audit Software, v5.0 (2014) Available for free download at http://www.awwa.org/resources-tools/water-knowledge/water-loss-control.aspx
EPA—U.S. Environmental Protection Agency. CUPSS—check up program for small systems. Available at http://water.epa.gov/infrastructure/drinkingwater/pws/cupss/index.cfm
Ramana GV, Chekka VSSS (2018) Validation and examination of existing water distribution network for continuous supply of water using EPANET. Water Resour Manage 32(6):1993–2011
Ormsbee LE (2018) The evolution of water distribution models. In: 1st International WDSA/CCWI joint conference proceedings, Kingston, Ontario. 23–25 July 2018
WSO—Water Systems Optimization, Inc. Secondary research for water leak detection program and water system loss control study. San Francisco, CA, December 2009
Tardelli Filho J (2005) Abastecimiento de Agua. Universidade de São Paulo, Brazil, Escola Politecnica
May J (1994) Leakage, pressure and control. In: BICS international conference on leakage control investigation in underground: assets, London
Thornton J, Sturm R, Kunkel G (2008) Water loss control manual, 2nd edn. McGraw Hill, New York
Kummu M, Guillaume JHA, de Moel H. Eisner S, Flörke M, Porkka M, Siebert S, Veldkamp TIE, Ward PJ (2016) The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Nat Sci Rep 6(38495). https://www.nature.com/articles/srep38495
Sanchez AS, Oliveira-Esquerre KP (2018) Internet of things for a smart campus: on-line monitoring of water consumption in university buildings. Int J Eng Res Technol 7:341–345
Farah E, Abdallah A, Shahrour I (2017) SunRise: large scale demonstrator of the smart water system. Int J Sus Dev Plann 12(1):112–121
Khrone Messtechnik GmbH. Waterflux 3070 Datasheet. Available at: https://krohne.com
Badger Meter, Inc. Managed AMI solution: Beacon® AMA. Available at: https://www.badgermeter.com/
Echologics and Mueller Co. EchoShore®-DX and EchoShore®-TX. Available at: https://www.echologics.com/
Hunn N (2017) Choosing a communications protocol for loggers. i2O Upstream, Southampton, UK. Released on 29 Sept 2017. Available at: https://en.i2owater.com/thought-piece/
Universal Metering Ltd. Smart meter prepayment™ brochure. http://www.universalmetering.co.uk/prepayment.htm
Walker L (2017) How does asset management software REALLY help the water industry? Water Finance Manage, June 19, 2017. Accessed 30 Oct 2018. https://waterfm.com
EPA—U.S. Environmental Protection Agency (2008) EPA’s asset management: a best practices guide office of water (4606 M), 2008
IAM—The Institute of Asset Management. Asset information, strategy, standards and data management. Version 1.1, Oct 2015
Lane A, Norton M, Ryan S (2017) Water resources: a new water architecture. Challenge in water management series. Wiley, Oxford
Nguyen KA, Stewart RA, Zhang H, Sahin O, Siriwardene N (2018) Re-engineering traditional urban water management practices with smart metering and informatics. Environ Model Softw 101:256–267
Gurung TR, Stewart RA, Sharma AK, Beal CD (2014) Smart meters for enhanced water supply network modelling and infrastructure planning. Resour Conserv Recycl 90:34–50
Dickey T (2018) Smart water solutions for smart cities. In: McClellan S, Jimenez J, Koutitas G (eds) Smart cities: applications, technologies, standards, and driving factors. Springer, Cham
i2O Water Ltd. OneT datasheet. Southampton, UK. Released on 20/02/2018. Available at https://en.i2owater.com/solutions/onet/
Lloyd Owen DA (2018) Smart water technologies and techniques: data capture and analysis for sustainable water management. Challenge in water management series. Wiley, Oxford. https://doi.org/10.1002/9781119078678
Russ Meir (2018) Handbook of knowledge management for sustainable water systems. Challenge in water management series. Wiley, Oxford
James G, Witten D, Hastie T, Tibshirani R (2015) An introduction to statistical learning with applications in R. Springer, New York
Britton TC, Stewart RA, O’Halloran KR (2013) Smart metering: enabler for rapid and effective post meter leakage identification and water loss management. J Clean Prod 54:166–176
Cominola A, Giuliani M, Piga D, Castelletti A, Rizzoli AE (2015) Benefits and challenges of using smart meters for advancing residential water demand modeling and management: a review. Environ Model Software 72:198–214
Gurung TR, Stewart RA, Beal CD, Sharma AK (2014) Smart meter enabled water end-use demand data: platform for the enhanced infrastructure planning of contemporary urban water supply networks. J Clean Prod 87(15):642–654
Nguyen KA, Stewart RA, Zhang H, Jones C (2015) Intelligent autonomous system for residential water end use classification: Autoflow. Appl Soft Comput 31:118–131
Hubert M, Vandevieren E (2008) An adjusted boxplot for skewed distributions. Comput Stat Data Anal 52(5):5186–5201
Lhango V, Subramanian R, Vasudevan V (2012) A five step procedure for outlier analysis in data mining. Eur J Sci Res 75(3):327–339
Bertsimas D, Pawlowski C, Zhuo YD (2017) From predictive methods to missing data imputation: an optimization approach. J Mach Learn Res 18(1):7133–7171
Wang Y, Chen Q, Hong T, Kang C (2018) Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans Smart Grid 1–24. https://doi.org/10.1109/TSG.2018.2818167
Söderberg A (2017) Turning smart water meter data into useful information a case study on rental apartments in Södertälje Philip Dahlström
Guancheng G, Shuming L, Yipeng W, Junyu L, Ren Z, Xiaoyun Z (2018) Short-term water demand forecast based on deep learning method. J Water Resour Plan Manage 144:4018076. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000992
Walker D, Creaco E, Vamvakeridou-Lyroudia L, Farmani R, Kapelan Z, Savić D (2015) Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks. Procedia Eng 119:1419–1428. https://doi.org/10.1016/j.proeng.2015.08.1002
Chena J, Boccelli DL (2014) Demand forecasting for water distribution systems. Procedia Eng 70:339–342. https://doi.org/10.1016/j.proeng.2014.02.038
Herrera M, Torgo L, Izquierdo J, Pérez-García R (2010) Predictive models for forecasting hourly urban water demand. J Hydrol 387:141–150. https://doi.org/10.1016/j.jhydrol.2010.04.005
Jain A, Kumar Varshney A, Chandra Joshi U (2001) Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks. Water Resour Manag 15:299–321. https://doi.org/10.1023/A:1014415503476
Brentan B, Meirelles G, Luvizotto E, Izquierdo J (2018) Joint operation of pressure-reducing valves and pumps for improving the efficiency of water distribution systems. J Water Resour Plan Manag 144:04018055. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000974
Montalvo I, Izquierdó J, Peerez-Garcia R, Herrera M (2010) Improved performance of PSO with self-adaptive parameters for computing the optimal design of water supply systems. Eng Appl Artif Intell 23:727–735. https://doi.org/10.1016/j.engappai.2010.01.015
Montalvo I, Izquierdo J, Pérez R, Iglesias PL (2008) A diversity-enriched variant of discrete PSO applied to the design of water distribution networks. Eng Optim 40:655–668. https://doi.org/10.1080/03052150802010607
Liong S, Atiquzzaman M (2004) Optimal design of water distribution network using shuffled complex evolution. Engineering 44:93–107
Sadeghioon AM, Metje N, Chapman D, Anthony C (2018) Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms. Urban Water J 15:287–295. https://doi.org/10.1080/1573062X.2018.1424213
Karray F, Garcia-Ortiz A, Jmal MW, Obeid AM, Abid M (2016) EARNPIPE: a testbed for smart water pipeline monitoring using wireless sensor network. Procedia Comput. Sci. 96:285–294. https://doi.org/10.1016/j.procs.2016.08.141
Candelieri A, Archetti F, Messina E (2013) Improving leakage management in urban water distribution networks through data analytics and hydraulic simulation. WIT Trans Ecol Environ 171:107–117. https://doi.org/10.2495/WRM130101
Eliades DG, Polycarpou MM (2012) Leakage fault detection in district metered areas of water distribution systems. J. Hydroinformatics 14:992. https://doi.org/10.2166/hydro.2012.109
Poulakis Z, Valougeorgis D, Papadimitriou C (2003) Leakage detection in water pipe networks using a Bayesian probabilistic framework. Probabilistic Eng Mech. 18:315–327. https://doi.org/10.1016/S02
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sánchez, A.S., Oliveira-Esquerre, K.P., dos Reis Nogueira, I.B., de Jong, P., Filho, A.A. (2020). Water Loss Management Through Smart Water Systems. In: Patnaik, S., Sen, S., Mahmoud, M. (eds) Smart Village Technology. Modeling and Optimization in Science and Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-37794-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-37794-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37793-9
Online ISBN: 978-3-030-37794-6
eBook Packages: EngineeringEngineering (R0)