Hosseiny, S. H., Bozorg-Haddad, O. & Bocchiola, D. in Economical, Political, and Social Issues in Water Resources (Ed. Bozorg-Haddad, O.) 189–216 (Elsevier, 2021).
Schreiber, K. J. & Rojas, J. L. The Puquios of Nasca. Lat. Am. Antiq. 6, 229–254 (1995).
Google Scholar
Coningham, R. & Young, R. The Archaeology of South Asia: From the Indus to Asoka, c.6500 BCE–200 CE 101–278 (Cambridge Univ. Press, 2015).
Wright, R. A Short History of Progress (House of Anansi, 2004).
Price, M. Origins of ancient Rome’s famed pipe plumbing system revealed in soil samples. Science (28 August 2017).
Bierkens, M. F. P. & Wada, Y. Non-renewable groundwater use and groundwater depletion: a review. Environ. Res. Lett. 14, 063002 (2019).
Google Scholar
Gordon, L., Dunlop, M. & Foran, B. Land cover change and water vapour flows: learning from Australia. Phil. Trans. R. Soc. Lond. B. Biol. Sci. B 29, 358 (2003).
Liu, W., Iordan, C. M., Cherubini, F., Hu, X. & Fu, D. Environmental impacts assessment of wastewater treatment and sludge disposal systems under two sewage discharge standards: a case study in Kunshan, China. J. Clean. Prod. 287, 125046 (2021).
Google Scholar
Elsaid, K. et al. Environmental impact of desalination technologies: a review. Sci. Total Environ. 748, 141528 (2020).
Google Scholar
da Silva, G. C. X. et al. Environmental impacts of dam reservoir filling in the East Amazon. Front. Water https://doi.org/10.3389/frwa.2020.00011 (2020).
UN-Water Summary Progress Update 2021: SDG 6—Water and Sanitation for All (United Nations, 2021).
IPCC Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).
Russell, S. J. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, 2020).
Rozos, E. Machine learning, urban water resources management and operating policy. Resources 8, 173 (2019).
Google Scholar
The Sustainable Development Goals Report 2022 (United Nations, 2022).
Yamazaki, D. & Trigg, M. A. The dynamics of Earth’s surface water. Nature 540, 348–349 (2016).
Google Scholar
Kadow, C., Hall, D. M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413 (2020).
Google Scholar
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
Google Scholar
Larson, A. A clearer view of Earth’s water cycle via neural networks and satellite data. Nat. Rev. Earth Environ. 3, 361 (2022).
Google Scholar
Koppa, A., Rains, D., Hulsman, P., Poyatos, R. & Miralles, D. G. A deep learning-based hybrid model of global terrestrial evaporation. Nat. Commun. 13, 1912 (2022).
Google Scholar
Espeholt, L. et al. Deep learning for twelve hour precipitation forecasts. Nat. Commun. 13, 5145 (2022).
Google Scholar
Muste, M., Kim, D. & Kim, K. A flood-crest forecast prototype for river floods using only in-stream measurements. Commun. Earth Environ. 3, 78 (2022).
Google Scholar
Vereecken, H. et al. Soil hydrology in the Earth system. Nat. Rev. Earth Environ. 3, 573–587 (2022).
Google Scholar
Shen, L. Q., Amatulli, G., Sethi, T., Raymond, P. & Domisch, S. Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. Sci. Data 7, 161 (2020).
Google Scholar
Podgorski, J. & Berg, M. Global analysis and prediction of fluoride in groundwater. Nat. Commun. 13, 4232 (2022).
Google Scholar
Sharafati, A., Asadollah, S. B. H. S. & Neshat, A. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J. Hydrol. 591, 125468 (2020).
Google Scholar
Zaniolo, M., Giuliani, M., Sinclair, S., Burlando, P. & Castelletti, A. When timing matters—misdesigned dam filling impacts hydropower sustainability. Nat. Commun. 12, 3056 (2021).
Google Scholar
Chakkaravarthy, G. V. & Lavanya, R. in Integrating AI in IoT Analytics on the Cloud for Healthcare Applications (ed. Jeya Mala, D.) 57–66 (IGI Global, 2022).
Mozo, A. et al. Chlorophyll soft-sensor based on machine learning models for algal bloom predictions. Sci Rep. 12, 13529 (2022).
Google Scholar
Massarelli, C., Campanale, C. & Uricchio, V. F. in IoT Applications Computing (eds Singh, I. et al.) Ch. 10 (IntechOpen, 2021).
Zarei, M. et al. Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages. Sci. Rep. 11, 24295 (2021).
Google Scholar
Riga, R. Brisbane 2011 flood victims win $440 million in class action partial settlement over operation of Wivenhoe Dam. ABC News https://web.archive.org/web/20130127022836/http://www.australiangeographic.com.au/journal/the-worst-floods-in-australian-history.htm (2021).
Oksen, P. & Favre, L. Innovative Technology in the Water, Sanitation and Hygiene (WASH) Sector (WIPO, 2020); https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gc_20_1.pdf
Fankhauser, K. et al. Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action. Sci. Total Environ. 831, 154453 (2022).
Google Scholar
Bauer, P. et al. The digital revolution of Earth-system science. Nat. Comput. Sci. 1, 104–113 (2021).
Google Scholar
Irrgang, C. et al. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. 3, 667–674 (2021).
Google Scholar
Hess, P., Drüke, M., Petri, S., Strnad, F. M. & Boers, N. Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nat. Mach. Intell. 4, 828–839 (2022).
Google Scholar
Fecht, S. Using artificial intelligence to locate risky dams. Columbia Climate School News https://news.climate.columbia.edu/2018/08/23/artificial-intelligence-find-risky-dams/ (2018).
van Vliet, M. T. H. et al. Global water scarcity including surface water quality and expansions of clean water technologies. Environ. Res. Lett. 16, 24020 (2021).
Google Scholar
Hosseiny, H., Nazari, F., Smith, V. & Nataraj, C. A framework for modeling flood depth using a hybrid of hydraulics and machine learning. Sci. Rep. 10, 8222 (2020).
Google Scholar
Perera, D., Smakhtin, V., Williams, S., North, T. & Curry, A. Ageing Water Storage Infrastructure: An Emerging Global Risk (UNU-INWEH, 2021); https://inweh.unu.edu/wp-content/uploads/2021/01/Ageing-Water-Storage-Infrastructure-An-Emerging-Global-Risk_web-version.pdf
Maleki, R. et al. Materials discovery of ion-selective membranes using artificial intelligence. Commun. Chem. 5, 132 (2022).
Google Scholar
Seo, D. H. et al. Anti-fouling graphene-based membranes for effective water desalination. Nat. Commun. 9, 683 (2018).
Google Scholar
Kim, H. et al. Adsorption-based atmospheric water harvesting device for arid climates. Nat. Commun. 9, 1191 (2018).
Google Scholar
Pham Vu Hong, S. & Nguyen Thanh, V. Application of artificial intelligence algorithm to optimize the design of water distribution system. Int. J. Constr. Manage. https://doi.org/10.1080/15623599.2022.2101593 (2022).
Tzachor, A., Sabri, S., Richards, C. E., Acuto, M. & Rajabifard, A. Potential and limitiations of digital twins to achieve the Sustainable Development Goals. Nat. Sustain. 5, 822–829 (2022).
Wong, T. H. F., Rogers, B. C. & Brown, R. R. Transforming cities through water-sensitive principles and practices. One Earth 3, 436–447 (2020).
Google Scholar
Li, L., Rong, S., Wang, R. & Yu, S. Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: a review. Chem. Eng. J. 405, 126673 (2021).
Google Scholar
Nguyen, X. C. et al. in Current Developments in Biotechnology and Bioengineering: Advances in Biological Wastewater Treatment Systems (eds Bui, X.-T. et al.) 587–608 (Elsevier, 2022).
Sakiewicz, P., Piotrowski, K., Ober, J. & Karwot, J. Innovative artificial neural network approach for integrated biogas—wastewater treatment system modelling: effect of plant operating parameters on process intensification. Renew. Sustain. Energy Rev. 124, 109784 (2020).
Google Scholar
Rani, A., Snyder, S. W., Kim, H., Lei, Z. & Pan, S.-Y. Pathways to a net-zero-carbon water sector through energy-extracting wastewater technologies. npj Clean Water 5, 49 (2022).
Google Scholar
How AI is taking SCADA systems to the next level. Veolia https://blog.veolianorthamerica.com/how-ai-taking-scada-systems-to-next-level (2022).
Increasing water pump efficiency using artificial intelligence. Melbourne Water https://www.melbournewater.com.au/water-data-and-education/news/research-and-innovation/increasing-water-pump-efficiency-using (2021).
Balla, K. M., Bendtsen, J. D., Schou, C., Kallesøe, C. S. & Ocampo-Martinez, C. A learning-based approach towards the data-driven predictive control of combined wastewater networks—an experimental study. Water Res. 221, 118782 (2022).
Google Scholar
Machine learning can help protect urban water. Here’s how. World Economic Forum https://www.weforum.org/agenda/2022/04/how-to-prevent-urban-water-stress-through-machine-learning/ (2022).
Vanijjirattikhan, R. et al. AI-based acoustic leak detection in water distribution systems. Results Eng. 15, 100557 (2022).
Google Scholar
Dawood, T., Elwakil, E., Novoa, H. M. & Delgado, J. F. G. Artificial intelligence for the modeling of water pipes deterioration mechanisms. Autom. Constr. 120, 103398 (2020).
Google Scholar
Zhou, B., Lau, V. & Wang, X. Machine-learning-based leakage-event identification for smart water supply systems. IEEE Internet Things J. 7, 2277–2292 (2020).
Google Scholar
Fu, G., Jin, Y., Sun, S., Yuan, Z. & Butler, D. The role of deep learning in urban water management: a critical review. Water Res. 223, 118973 (2022).
Google Scholar
Harnessing the Fourth Industrial Revolution for Water (World Economic Forum, 2018).
Stańczyk, J., Kajewska-Szkudlarek, J., Lipiński, P. & Rychlikowski, P. Improving short-term water demand forecasting using evolutionary algorithms. Sci. Rep. 12, 13522 (2022).
Google Scholar
Food and Agriculture Organization. Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal). World Bank Data https://data.worldbank.org/indicator/ER.H2O.FWAG.ZS (2018).
Tzachor, A., Richards, C. E. & Jeen, S. Transforming agrifood production systems and supply chains with digital twins. npj Sci. Food 6, 47 (2022).
Google Scholar
Dehghanisanij, H., Emami, H., Emami, S. & Rezaverdinejad, V. A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture. Sci. Rep. 12, 6728 (2022).
Google Scholar
Bauer, A. et al. Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: a case study of lettuce production. Hortic. Res. 6, 70 (2019).
Google Scholar
García, L., Rodríguez, J. D., Wijnen, M. & Pakulski, I. Earth Observation for Water Resources Management: Current Use and Future Opportunities for the Water Sector (World Bank, 2016).
Cominola, A. et al. Long-term water conservation is fostered by smart meter-based feedback and digital user engagement. npj Clean Water 4, 29 (2021).
Google Scholar
Harnessing Artificial Intelligence for the Earth (World Economic Forum, 2018).
Bedell, E., Harmon, O., Fankhauser, K., Shivers, Z. & Thomas, E. A continuous, in-situ, near-time fluorescence sensor coupled with a machine learning model for detection of fecal contamination risk in drinking water: design, characterization and field validation. Water Res. 220, 118644 (2022).
Google Scholar
Andres, L., Boateng, K., Borja-Vega, C. & Thomas, E. A review of in-situ and remote sensing technologies to monitor water and sanitation interventions. Water https://doi.org/10.3390/w10060756 (2018).
Swan, A., Cooper, N., Gamble, W. & Pritchard, M. Using smart pumps to help deliver universal access to safe and affordable drinking water. Proc. Inst. Civ. Eng. Eng. Sustain. 171, 277–285 (2017).
Aiken, E., Bellue, S., Karlan, D., Udry, C. & Blumenstock, J. E. Machine learning and phone data can improve targeting of humanitarian aid. Nature 603, 864–870 (2022).
Google Scholar
Pandey, R. et al. A machine learning application for raising WASH awareness in the times of COVID-19 pandemic. Sci. Rep. 12, 810 (2022).
Google Scholar
De Santi, M. et al. Modelling point-of-consumption residual chlorine in humanitarian response: can cost-sensitive learning improve probabilistic forecasts? PLoS Water 1, e0000040 (2022).
Google Scholar
Progress on Household Drinking Water, Sanitation and Hygiene 2000–2020: Five Years Into the SDGs (World Health Organization and United Nations Children’s Fund, 2021).
Guenat, S. et al. Meeting sustainable development goals via robotics and autonomous systems. Nat. Commun. 13, 3559 (2022).
Google Scholar
Slow pace of innovation continues to frustrate supply chain. British Water https://www.britishwater.co.uk/news/602860/Slow-pace-of-innovation-continues-to-frustrate-supply-chain-.htm (2022).
Without universal AI literacy, AI will fail us. World Economic Forum https://www.weforum.org/agenda/2022/03/without-universal-ai-literacy-ai-will-fail-us/ (2022).
Brundage, M. et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation. Apollo https://doi.org/10.17863/CAM.22520 (2018)
Tzachor, A., Devare, M., King, B., Avin, S. & Ó hÉigeartaigh, S. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat. Mach. Intell. 4, 104–109 (2022).
Google Scholar
Amodei, D. et al. Concrete problems in AI safety. Preprint at https://doi.org/10.48550/arXiv.1606.06565 (2016).
Trávníček, P., Junga, P., Kotek, L. & Vítěz, T. Analysis of accidents at municipal wastewater treatment plants in Europe. J. Loss Prev. Process Ind. 74, 104634 (2022).
Google Scholar
Doorn, N. Artificial intelligence in the water domain: opportunities for responsible use. Sci. Total Environ. 755, 142561 (2021).
Google Scholar
Rulli, M. C., Saviori, A. & D’Odorico, P. Global land and water grabbing. Proc. Natl Acad. Sci. USA 110, 892–897 (2013).
Google Scholar
Gallagher, R. UK water supplier hit by ‘extremely concerning’ cyberattack. Bloomberg. https://www.bloomberg.com/news/articles/2022-08-17/uk-water-supplier-hit-by-extremely-concerning-cyberattack (2022).
Taddeo, M., McCutcheon, T. & Floridi, L. Trusting artificial intelligence in cybersecurity is a double-edged sword. Nat. Mach. Intell. 1, 557–560 (2019).
Google Scholar
Milne, S. How water shortages are brewing wars. BBC Future https://www.bbc.com/future/article/20210816-how-water-shortages-are-brewing-wars (2021).
Omerspahic, M., Al-Jabri, H., Siddiqui, S. A. & Saadaoui, I. Characteristics of desalination brine and its impacts on marine chemistry and health, with emphasis on the Persian/Arabian Gulf: a review. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.845113 (2022).
Zaidi, S. M. A. et al. Machine learning for energy–water nexus: challenges and opportunities. Big Earth Data 2, 228–267 (2018).
Google Scholar
Patterson, D. A. et al. Carbon emissions and large neural network training. Preprint at https://arxiv.org/abs/2104.10350 (2021).
Kaack, L. H. et al. Aligning artificial intelligence with climate change mitigation. Nat. Clim. Change 12, 518–527 (2022).
Google Scholar
Alonso, C., Kothari, S. & Rehman, S. How Artificial Intelligence Could Widen the Gap between Rich and Poor Nations (International Monetary Fund, 2020).
Sarni, W., White, C., Webb, R., Cross, K. & Glotzbach, R. Digital Water: Industry lEaders Chart the Transformation Journey (IWA, 2019); https://iwa-network.org/wp-content/uploads/2019/06/IWA_2019_Digital_Water_Report.pdf
Barredo Arrieta, A. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).
Google Scholar
Gunning, D. et al. XAI—explainable artificial intelligence. Sci. Robot. 4, eaay7120 (2019).
Google Scholar
Ethics guidelines for trustworthy AI. European Commission https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (2023).
Avin, S. et al. Filling gaps in trustworthy development of AI. Science 374, 1327–1329 (2021).
Google Scholar
Brundage, M. et al. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020).
AI Governance: A Holistic Approach to implement Ethics into AI (World Economic Forum, 2019).
Davenport, T. H. & Ronanki, R. Artificial intelligence for the real world: don’t start with moon shots. Harvard Business Review https://www.hbsp.harvard.edu/product/R1801H-PDF-ENG (2018).
Martineau, P. Toronto tapped artificial intelligence to warn swimmers. The experiment failed. The Information https://www.theinformation.com/articles/when-artificial-intelligence-isnt-smarter (2022).
