Cyber-physical systems in water management and governance
Open accessAbstract
Water governance is facing rapid transformations as cyber-physical systems (CPS) are deployed across water-related sectors and river basins. These CPS — often considered as artificial intelligence-enabled, automated or ‘smart’ technological systems — are promoted for improving monitoring, management and governance of hydrological systems. We review recent applications of CPS, highlighting their diverse functions across the water cycle, including in rural, urban and coastal settings. We then focus on how smart technologies connect to people, policy and ecosystems. Key to our argument is that integrating the social and ecosystem dimensions into CPS research and design will be vital for sustainable transformations in water management and governance, as per a cybernetic approach. This includes consideration of social data requirements, end-user experience, sociopolitical and environmental impacts, as well as acceptability, of CPS.
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Moving any digital model closer to accurately representing an actual process is a fundamental basis for creating digital twin models.
“Moving any digital model closer to accurately representing an actual process is a fundamental basis for creating DTM [6,24].”
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Cites
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480 · see citing works
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203 · see citing works
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178 · see citing works
How to stop data centres from gobbling up the world’s electricity
Jones, N. (2018) · Nature · doi:10.1038/d41586-018-06610-y · Cited by 1,166 · see citing works
The hydrosocial cycle: Defining and mobilizing a relational-dialectical approach to water
Linton, J., & Budds, J. (2013) · Geoforum · doi:10.1016/j.geoforum.2013.10.008 · Cited by 1,139 · see citing works
Sustainable AI: AI for sustainability and the sustainability of AI
van Wynsberghe, A. (2021) · AI and Ethics · doi:10.1007/s43681-021-00043-6 · Cited by 939 · see citing works
(2015) · neural information processing systems · Cited by 912 · see citing works
The paradox of irrigation efficiency
Grafton, R., Williams, J., Perry, C., Molle, F., Ringler, C., Steduto, P., Udall, B., Wheeler, S., Wang, Y., Garrick, D., et al. (2018) · Science · doi:10.1126/science.aat9314 · Cited by 848 · see citing works
Cybernetics-or control and communication in the animal and the machine
Charp, S. (1949) · Journal of the Franklin Institute · doi:10.1016/0016-0032(49)90745-0 · Cited by 672 · see citing works
Atlas of AI: power, politics, and the planetary costs of artificial intelligence.
Can, M. (2022) · International Affairs · doi:10.1093/ia/iiac034 · Cited by 614 · see citing works
The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations
Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G. S., & Friday, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100340 · Cited by 570 · see citing works
Artificial intelligence, systemic risks, and sustainability
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., et al. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101741 · Cited by 497 · see citing works
Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity
Zeadally, S., Adi, E., Baig, Z. A., & Khan, I. A. (2020) · IEEE Access · doi:10.1109/access.2020.2968045 · Cited by 334 · see citing works
The ai-based cyber threat landscape: A survey
Kaloudi, N., & Li, J. (2021) · ACM Computing Surveys · doi:10.1145/3372823 · Cited by 310 · see citing works
Data-driven artificial intelligence applications for sustainable precision agriculture
Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., G. Olaizola, I., Barriguinha, A., Moysiadis, T., et al. (2021) · Agronomy · doi:10.3390/agronomy11061227 · Cited by 310 · see citing works
Digital Twins in Agriculture: A State-of-the-art review
Purcell, W., & Neubauer, T. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100094 · Cited by 303 · see citing works
Koech, R., & Langat, P. (2018) · Water · doi:10.3390/w10121771 · Cited by 260 · see citing works
Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing
Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020) · IET Collaborative Intelligent Manufacturing · doi:10.1049/iet-cim.2020.0009 · Cited by 256 · see citing works
Lund, N. S. V., Falk, A. K. V., Borup, M., et al. (2018) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2018.1455484 · Cited by 221 · see citing works
Machine Learning for Soft Robotic Sensing and Control
Chin, K., Hellebrekers, T., & Majidi, C. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.201900171 · Cited by 215 · see citing works
Cyber-Physical-Social Systems: A State-of-the-Art Survey, Challenges and Opportunities
Zhou, Y., Yu, F. R., Chen, J., & Kuo, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2959013 · Cited by 206 · see citing works
Dafflon, B., Moalla, N., & Ouzrout, Y. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06572-4 · Cited by 194 · see citing works
A new drone-borne GPR for soil moisture mapping
Wu, K., Rodriguez, G. A., Zajc, M., Jacquemin, E., Clément, M., Coster, A. D., & Lambot, S. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111456 · Cited by 181 · see citing works
Ahlborg, H., Ruiz-Mercado, I., Molander, S., & Masera, O. (2019) · Sustainability · doi:10.3390/su11072009 · Cited by 172 · see citing works
Boundary matters: the potential of system dynamics to support sustainability?
Nabavi, E., Daniell, K. A., & Najafi, H. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.03.032 · Cited by 162 · see citing works
Self-citationDigital Twins in agriculture: challenges and opportunities for environmental sustainability
Purcell, W., Neubauer, T., & Mallinger, K. (2023) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2022.101252 · Cited by 160 · see citing works
Ramotsoela, D. T., Abu-Mahfouz, A. M. I., & Hancke, G. P. (2018) · Sensors · doi:10.3390/s18082491 · Cited by 158 · see citing works
Smart Water Management towards Future Water Sustainable Networks
Ramos, H. M., McNabola, A., Jiménez, P. A. L., et al. (2019) · Water · doi:10.3390/w12010058 · Cited by 157 · see citing works
The Fourth-Revolution in the Water Sector Encounters the Digital Revolution
Garrido‐Baserba, M., Corominas, L., Ulises Cortés, A., Rosso, D., & Poch, M. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.9b04251 · Cited by 127 · see citing works
Gray, P. C., Ridge, J. T., Poulin, S. K., et al. (2018) · Remote Sensing · doi:10.3390/rs10081257 · Cited by 118 · see citing works
Guzman, N. H. C., Wied, M., KOZINE, I. O., & Lundteigen, M. A. (2019) · Systems Engineering · doi:10.1002/sys.21509 · Cited by 113 · see citing works
Kotamäki, N., Thessler, S., Koskiaho, J., et al. (2009) · Sensors · doi:10.3390/s90402862 · Cited by 102 · see citing works
Open storm: a complete framework for sensing and control of urban watersheds
Bartos, M., Wong, B. P., & Kerkez, B. (2017) · Environmental Science Water Research & Technology · doi:10.1039/c7ew00374a · Cited by 99 · see citing works
Water governance across competing scales: Coupling land and water management
Daniell, K. A., & Barreteau, O. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2014.10.055 · Cited by 89 · see citing works
Self-citationMatheri, A. N., Belaid, M., Ntuli, F., et al. (2022) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2022.103152 · Cited by 85 · see citing works
Radini, S., Marinelli, E., Akyol, Ç., et al. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117268 · Cited by 83 · see citing works
Urban Hydroinformatics: Past, Present and Future
Makropoulos, C., & Savic, D. A. (2019) · Water · doi:10.3390/w11101959 · Cited by 75 · see citing works
Science in support of systematic leadership towards sustainability
Broman, G. I., Robèrt, K., Collins, T. J., Basile, G., Baumgartner, R. J., Larsson, T. C., & Huisingh, D. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.09.085 · Cited by 74 · see citing works
Smart Sensing for Agriculture: Applications, Advancements, and Challenges
Kumar, R., Mishra, R., Gupta, H. P., et al. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3049623 · Cited by 70 · see citing works
Ipsen, K. L., Zimmermann, R. K., Nielsen, P. S., et al. (2018) · The International Journal of Life Cycle Assessment · doi:10.1007/s11367-018-1453-9 · Cited by 62 · see citing works
Smart water grid: A smart methodology to detect leaks in water distribution networks
Fabbiano, L., Vacca, G., & Dinardo, G. (2019) · Measurement · doi:10.1016/j.measurement.2019.107260 · Cited by 55 · see citing works
Real-Time Control of Urban Water Cycle under Cyber-Physical Systems Framework
Sun, C., Puig, V., & Cembrano, G. (2020) · Water · doi:10.3390/w12020406 · Cited by 55 · see citing works
Internet of Things Enhanced User Experience for Smart Water and Energy Management
Curry, E., Hasan, S., Kouroupetroglou, C., Fabritius, W., Hassan, U. U., & Derguech, W. (2018) · IEEE Internet Computing · doi:10.1109/mic.2018.011581514 · Cited by 54 · see citing works
An IoT-enabled portable sensing system with MWCNTs/PDMS sensor for nitrate detection in water
Akhter, F., Siddiquei, H. R., Alahi, M. E. E., et al. (2021) · Measurement · doi:10.1016/j.measurement.2021.109424 · Cited by 51 · see citing works
Ison, R. L., Alexandra, J., & Wallis, P. (2018) · Sustainability Science · doi:10.1007/s11625-018-0570-5 · Cited by 49 · see citing works
Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT
Mohapatra, H., & Rath, A. K. (2019) · IET Wireless Sensor Systems · doi:10.1049/iet-wss.2019.0081 · Cited by 47 · see citing works
A real time data driven algal bloom risk forecast system for mariculture management
Guo, J., Dong, Y., & Lee, J. H. W. (2020) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2020.111731 · Cited by 47 · see citing works
Technology, nature’s appropriation and capital accumulation in modern agriculture
Gras, C., & Cáceres, D. (2020) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2020.04.001 · Cited by 46 · see citing works
Godden, L. C., & Ison, R. L. (2019) · Australasian Journal of Water Resources · doi:10.1080/13241583.2019.1608688 · Cited by 42 · see citing works
Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
Adedeji, K. B., & Hamam, Y. (2020) · Sustainability · doi:10.3390/su12229555 · Cited by 41 · see citing works
Rethinking Resilience Analytics
Eisenberg, D. A., Seager, T. P., & Alderson, D. (2019) · Risk Analysis · doi:10.1111/risa.13328 · Cited by 37 · see citing works
Smart water metering: adoption, regulatory and social considerations
Koech, R., Cardell‐Oliver, R., & Syme, G. J. (2021) · Australasian Journal of Water Resources · doi:10.1080/13241583.2021.1983968 · Cited by 34 · see citing works
Jia, Y. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07437-5 · Cited by 33 · see citing works
Khalid, A., & Ferreira, C. M. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104748 · Cited by 33 · see citing works
When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward
Song, J., Lyu, D., Zhang, Z., Wang, Z., Zhang, T., & Ma, L. (2022) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1145/3510457.3513049 · Cited by 33 · see citing works
Resilience of Cyber-Physical Systems: Role of AI, Digital Twins, and Edge Computing
Jin, A. S., Hogewood, L., Fries, S., Lambert, J. H., Fiondella, L., Strelzoff, A., Boone, J., Fleckner, K., & Linkov, I. (2022) · IEEE Engineering Management Review · doi:10.1109/emr.2022.3172649 · Cited by 33 · see citing works
Cyber-physical systems as sociotechnical systems: a view towards human–technology interaction
Kant, V. (2016) · Cyber-Physical Systems · doi:10.1080/23335777.2017.1289983 · Cited by 30 · see citing works
Wyrwoll, P., Grafton, R., Daniell, K., Chu, H.-A.-L., Ringler, C., Lien, L., Dang, K., Do, T. N., & Nguyen, T. D. (2018) · Earth s Future · doi:10.1002/2017ef000777 · Cited by 30 · see citing works
Self-citationSmart Sensor Node of WSNs for River Water Pollution Monitoring System
Kadir, E. A., Siswanto, A., Rosa, S. L., et al. (2019) · International Conference on Advanced Communication Technologies and Networking (CommNet) · doi:10.1109/commnet.2019.8742371 · Cited by 28 · see citing works
Multiscale modeling of backward erosion piping in flood protection system infrastructure
Fascetti, A., & Oskay, C. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12489 · Cited by 26 · see citing works
Holman, M., Walker, G. H., Lansdown, T. C., & Hulme, A. (2019) · Ergonomics · doi:10.1080/00140139.2019.1694173 · Cited by 26 · see citing works
IoT based Disaster Monitoring and Management System for Dams (IDMMSD)
Varghese, A. J., Jolly, A. T., Peter, A., et al. (2019) · doi:10.1109/iciict1.2019.8741464 · Cited by 24 · see citing works
Real-time river monitoring supports community management of low-flow periods
Ellison, J., Smethurst, P. J., Morrison, B., et al. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.03.035 · Cited by 23 · see citing works
Improving SIEM for critical SCADA water infrastructures using machine learning
Hindy, H., Brosset, D., Bayne, E., Seeam, A. K., & Bellekens, X. (2019) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 22 · see citing works
Masseroni, D., Arbat, G., & de Lima, M. I. P. (2020) · Water · doi:10.3390/w12010263 · Cited by 21 · see citing works
Letcher, T. M. (2022) · Elsevier eBooks · doi:10.1016/c2021-0-00376-x · Cited by 20 · see citing works
Cyber-Physical Systems for Smart Water Networks: A Review
Bhardwaj, J., Krishnan, J., Larios, D. F., Beferull‐Lozano, B., Cenkeramaddi, L. R., & Harman, C. R. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3121506 · Cited by 19 · see citing works
Cyber-physical-system-based smart water system to prevent flood hazards
Yang, T.-H., Yang, S.-C., Kao, H.-M., et al. (2018) · Smart Water · doi:10.1186/s40713-018-0008-3 · Cited by 18 · see citing works
Nikkels, M. J., Kumar, S., & Meinke, H. (2019) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2019.09.001 · Cited by 17 · see citing works
Design strategy to maximize recovery energy towards smart water grids: case study
Pérez‐Sánchez, M., Ferreira, A., Jiménez, P. A. L., et al. (2018) · Urban Water Journal · doi:10.1080/1573062x.2018.1459747 · Cited by 16 · see citing works
The datafication of water infrastructure and its implications for (il)legible water consumers
Hoefsloot, F. I., Richter, C., Martínez, J. A., & Pfeffer, K. (2022) · Urban Geography · doi:10.1080/02723638.2021.2019499 · Cited by 15 · see citing works
Legal Implications of the Use of Big Data in the Transboundary Water Context
Ibrahim, I. A. (2020) · Water Resources Management · doi:10.1007/s11269-020-02491-x · Cited by 12 · see citing works
Managing river basins with thinking machines
Muhammad, A. (2016) · IEEE Conference on Norbert Wiener in the 21st Century (21CW) · doi:10.1109/norbert.2016.7547453 · Cited by 11 · see citing works
Using Cyber Physical Systems to Map Water Quality Over Large Water Bodies
Hirani, P., Balivada, S. R., Chauhan, R., et al. (2018) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2018.8589560 · Cited by 9 · see citing works
Water systems and disruptions: the ‘old abnormal’?
Daniell, K. A. (2020) · Australasian Journal of Water Resources · doi:10.1080/13241583.2020.1780732 · Cited by 9 · see citing works
Self-citationWhat’s next for Australia’s water management?
Daniell, K. A., & Daniell, T. M. (2019) · Australasian Journal of Water Resources · doi:10.1080/13241583.2019.1696033 · Cited by 8 · see citing works
Self-citationArtificial Intelligence: For Better Or Worse
Wouters, N., Blashki, G. A., & Sykes, H. (2019) · Minerva Access (University of Melbourne) · Cited by 8 · see citing works
Fornarelli, R., Anda, M., Dallas, S., et al. (2021) · Water Science & Technology Water Supply · doi:10.2166/ws.2021.266 · Cited by 8 · see citing works
Rizou, S., Kenda, K., Kofinas, D., et al. (2018) · doi:10.3390/proceedings2110695 · Cited by 7 · see citing works
Making Lake Erie Smart by Driving Innovations in Technology and Networking
Pearson, B., Kearns, T., Slawecki, T., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00731 · Cited by 7 · see citing works
Lee, J., Choi, J. U., Jung, H., et al. (2019) · Journal of Coastal Research · doi:10.2112/si91-039.1 · Cited by 4 · see citing works
Creating a New Engineering Discipline for the Age of AI
Bell, G., & Euchner, J. (2022) · Research-Technology Management · doi:10.1080/08956308.2022.2021715 · Cited by 4 · see citing works
Tan, F. J., Uy, F. A. A., Cruz, F. R. G., et al. (2020) · IEEE Conference on Technologies for Sustainability · doi:10.1109/sustech47890.2020.9150517 · Cited by 3 · see citing works
Monitoring the State of Materials in Cyberphysical Systems: Water Supply Case Study
Desnitsky, V. A., & Kotenko, I. V. (2019) · Materials Today Proceedings · doi:10.1016/j.matpr.2019.01.005 · Cited by 2 · see citing works
Koutiva, I., & Makropoulos, C. (2021) · Computational Water Energy and Environmental Engineering · doi:10.4236/cweee.2021.104011 · Cited by 2 · see citing works
Research on the Smart Information System of Weihe River Basin
Yang, S., Jiang, R., Xie, J., et al. (2019) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/234/1/012006 · Cited by 1 · see citing works
Leenhardt, D., Demarez, V., Garin, P., et al. (2018) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.15454/vcipqy · Cited by 1 · see citing works
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- Authors
- Alexandra, C., Daniell, K. A., Guillaume, J., Saraswat, C., & Feldman, H. R.
- Affiliations
- Australian National University (AU)
- Corresponding author
- Daniell, Katherine A
- Venue
- Current Opinion in Environmental Sustainability
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 62
- Pages
- 101290
- ISSN
- 1877-3435 (ISSN-L)
- OpenAlex
- W4366765216
- Cited by
- 29
- References
- 87
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 11 · 2023: 1
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Environmental Sustainability (publishedVersion, cc-by-nc-nd)
- Funders
- European Commission (620633-EPP-1-2020-1-AU-EPPJMO-PROJECT)
- Australian Research Council (DE190100317, 620633-EPP-1-2020-1-AU-EPPJMO-PROJECT, 600145-EPP-1-2018-1-AU-EPPJMO-PROJECT)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Social Sciences › Political Science and International Relations › Water Governance and Infrastructure
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- cyber-physical systems, water governance, water management, smart technologies, artificial intelligence, hydrological monitoring, social dimensions of technology, cybernetics, technology acceptability, socio-political impact, ecosystem impacts, end-user experience, sustainability transformation, river basins
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