Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Muttungal, Peter V.; George, Jossy P.; Priya, Nishi; Davidson, Benny Godwin J. Title A name given to the resource Water Sustainability and Smart Monitoring: IOT and AI for Water Use Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived World Sustainability Series;Volume;Part F1465;pp.87-101 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-13412-7_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-13412-7_6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030243913?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030243913?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Muttungal P.V., Department of Psychology, Christ University, Bangalore, India; George J.P., Christ University, Bangalore, India; Priya N., Department of Psychology, Christ University, Bangalore, India; Davidson B.G.J., School of Business, University of the Fraser Valley, Abbotsford, BC, Canada Description An account of the resource This chapter discusses the contributions of Artificial Intelligence (AI) and the Internet of Things (IoT) to sustainable water management. It considers how traditional water management, including reservoirs, qanats, and aqueducts, has provided important lessons in climate adaptation and sustainability, while contemporary technologies effectively address challenges of scarcity, leakage, and inefficient management. AI-supported predictive analytics, IoT-enabled smart sensors, together with precision irrigation, can support leak detection and water demand forecasting, detecting soil moisture through sensors, and water quality monitoring. The chapter also touches on trends such as blockchain capabilities, circular water systems, and the use of citizen science. It offers new opportunities to innovate, focus on global partnerships, and small-scale capabilities, to help combine ancient wisdom with innovation to promote equitable, efficient, and durable approaches to water sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Artificial Intelligence; Internet of Things; Water management; Water sustainability Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21997373; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online