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Draft:Intelligent Water System

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An Intelligent Water System (IWS)[1][2][3] is an integrated water management platform that employs networks of sensors, actuators, communications infrastructure, and automated controls to to monitor, manage, and optimize water distribution and treatment. This practice is often referred to as smart water management[4][5][6] and is reflective of the Digital Transformation in Water Utilities.[6][4] This transformation and IWSs build on advances in IoT technology and Artificial Intelligence; aiming to enhance operational efficiency, provide predictive maintenance guidance, and conserve resources. However these same technologies incur vulnerabilities operators must be aware of, and pro-actively safeguard from cyber-incidents.[7]

Concerns Regarding Cyberbiosecurity

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IWSs being networked, and critical components of modern infrastructure; cyberbiosecurity is a major concern.[4][8][9][10][11] Increased reliance on networked devices for monitoring and control necessitates reliable methods to detect anomalies indicating potential cyber-incidents[12][13] and/or regulatory violations[14]. Facilities choose to adopt these technologies and associated risks to achieve goals such as improving operational efficiency, cost reduction, elevating public health standards, supporting smart irrigation practices, and capturing deeper insights to support achieving these aforementioned goals through data analysis.[15]

Artificial Intelligence in Intelligent Water Systems

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Artificial Intelligence (AI) solutions have emerged as an approach to secure IWSs, in addition to other critical infrastructure such as nuclear plants, smart grids and smart farms.[4] In water treatment and management applications, AI is used to support informed decision making and anomaly detection that could indicate cyber-incidents.[4][9] Researchers have developed comprehensive models intended to forecast water requirements & optimize system management to meet demand while minimizing resource use; with some including detection capabilities aiding in securing IWSs.[9][16] AI can also provide greater insights to policymakers responsible for regulations affecting water quality, that IWSs must abide by.[4] This practice offers researchers and policymakers the ability to understand the effects of past regulatory changes and improve future policy decisions.[17][18][19]

References

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  1. ^ "Viewing 4949-Intelligent-Water-Systems.pdf" (PDF). The Water Research Foundation. 2025-10-14. Retrieved 2025-10-01.
  2. ^ "Intelligent Water Systems : Water Environment Solutions : Hitachi". www.hitachi.com. Retrieved 2025-09-29.
  3. ^ Federation, Water Environment (2017-03-31). "Intelligent Water Systems: The Path to a Smart Utility". Access Water.
  4. ^ a b c d e f Batarseh, Feras A.; Kulkarni, Ajay (March 2023). "AI for Water". Computer. 56 (3): 109–113. Bibcode:2023Compr..56c.109B. doi:10.1109/MC.2022.3231142. ISSN 1558-0814.
  5. ^ Zhu, Ziang; Zhang, Han; Zhang, Shubo; Wang, Jinfeng; Ren, Hongqiang (2025), Garg, Manoj Chandra; Rajput, Vishnu D.; Minkina, Tatiana; Himanshu, Sushil Kumar (eds.), "Intelligent Monitoring and Control Systems for Smart Water Management", Nano-solutions for Sustainable Water and Wastewater Management: From Monitoring to Treatment, Cham: Springer Nature Switzerland, pp. 369–390, doi:10.1007/978-3-031-82794-5_16, ISBN 978-3-031-82794-5, retrieved 2025-09-30
  6. ^ a b "IEEE Smart Water Management". smartcities.ieee.org. Retrieved 2025-09-30.
  7. ^ Dada, Michael Ayorinde; Majemite, Michael Tega; Obaigbena, Alexander; Daraojimba, Onyeka Henry; Oliha, Johnson Sunday; Nwokediegwu, Zamathula Queen Sikhakhane (2024). "Review of smart water management: IoT and AI in water and wastewater treatment". World Journal of Advanced Research and Reviews. 21 (1): 1373–1382. doi:10.30574/wjarr.2024.21.1.0171. ISSN 2581-9615.
  8. ^ Sobien, Daniel; Kulkarni, Ajay; Batarseh, Feras A. (2025-02-01). "Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AI". Journal of Water Resources Planning and Management. 151 (2): 04024069. doi:10.1061/JWRMD5.WRENG-6488.
  9. ^ a b c Kulkarni, Ajay; Yardimci, Mehmet; Kabir Sikder, Md Nazmul; Batarseh, Feras A. (2023-09-01). "P2O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants". Journal of Environmental Engineering. 149 (9): 04023045. doi:10.1061/JOEEDU.EEENG-7266.
  10. ^ Hassanzadeh, Amin; Rasekh, Amin; Galelli, Stefano; Aghashahi, Mohsen; Taormina, Riccardo; Ostfeld, Avi; Banks, M. Katherine (2020-05-01). "A Review of Cybersecurity Incidents in the Water Sector". Journal of Environmental Engineering. 146 (5): 03120003. doi:10.1061/(ASCE)EE.1943-7870.0001686. ISSN 1943-7870.
  11. ^ Sobien, Daniel; Yardimci, Mehmet O.; Nguyen, Minh B. T.; Mao, Wan-Yi; Fordham, Vinita; Rahman, Abdul; Duncan, Susan; Batarseh, Feras A. (2023), Greenbaum, Dov (ed.), "AI for Cyberbiosecurity in Water Systems—A Survey", Cyberbiosecurity: A New Field to Deal with Emerging Threats, Cham: Springer International Publishing, pp. 217–263, doi:10.1007/978-3-031-26034-6_13, ISBN 978-3-031-26034-6, retrieved 2025-10-07
  12. ^ Yardimci, Mehmet Oguz; Batarseh, Feras A. (February 2025). "Towards Securing Water Systems Communications from Adversarial Attacks using AI Assurance". 2025 5th IEEE Middle East and North Africa Communications Conference (MENACOMM). pp. 1–7. doi:10.1109/MENACOMM62946.2025.10911020. ISBN 979-8-3315-1995-7.
  13. ^ Taormina, Riccardo; Galelli, Stefano; Tippenhauer, Nils Ole; Salomons, Elad; Ostfeld, Avi; Eliades, Demetrios G.; Aghashahi, Mohsen; Sundararajan, Raanju; Pourahmadi, Mohsen; Banks, M. Katherine; Brentan, B. M.; Campbell, Enrique; Lima, G.; Manzi, D.; Ayala-Cabrera, D. (2018-08-01). "Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks". Journal of Water Resources Planning and Management. 144 (8): 04018048. doi:10.1061/(ASCE)WR.1943-5452.0000969. ISSN 1943-5452.
  14. ^ Wang, Yingjie; Sobien, Dan; Kulkarni, Ajay; Batarseh, Feras A. (2024). "Quantifying water effluent violations and enforcement impacts using causal AI". Policy & Internet. 16 (2): 317–338. doi:10.1002/poi3.402. ISSN 1944-2866.
  15. ^ Bradham, David (2024-08-26). "Five Benefits of Smart Water Management". Banyan Water. Retrieved 2025-09-30.
  16. ^ Sharma, Sunil Kumar (2022-05-15). "A novel approach on water resource management with Multi-Criteria Optimization and Intelligent Water Demand Forecasting in Saudi Arabia". Environmental Research. 208 112578. Bibcode:2022ER....20812578S. doi:10.1016/j.envres.2021.112578. ISSN 0013-9351. PMID 34951989.
  17. ^ Park, Jungsu; Lee, Woo Hyoung; Kim, Keug Tae; Park, Cheol Young; Lee, Sanghun; Heo, Tae-Young (2022-08-01). "Interpretation of ensemble learning to predict water quality using explainable artificial intelligence". Science of the Total Environment. 832 155070. Bibcode:2022ScTEn.83255070P. doi:10.1016/j.scitotenv.2022.155070. ISSN 0048-9697. PMID 35398119.
  18. ^ Abdulameer, Layth; Al-Khafaji, Mahmoud Saleh; Al-Awadi, Aysar Tuma; Al Maimuri, Najah M. L.; Al-Shammari, Musa; Al-Dujaili, Ahmed N.; DhiyaAl‑Jumeily (2025-04-15). "Artificial Intelligence in Climate-Resilient Water Management: A Systematic Review of Applications, Challenges, and Future Directions". Water Conservation Science and Engineering. 10 (1): 44. Bibcode:2025WCSE...10...44A. doi:10.1007/s41101-025-00371-2. ISSN 2364-5687.
  19. ^ He, Minxue; Rozos, Evangelos (2025-06-27). "Editorial: Harnessing artificial intelligence to address climate-induced challenges in water resources management". Frontiers in Water. 7 1637341. Bibcode:2025FrWat...737341H. doi:10.3389/frwa.2025.1637341. ISSN 2624-9375.
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