Machine Learning-Driven Framework for Sustainable Water Management in Smart Cities

DOI

10.2991/978-94-6239-693-7_42How to use a DOI?

Keywords

Sustainable Water Management; Smart Cities; Machine Learning; Wastewater Treatment; Urban Water Systems; Data-Driven Decision Making

Abstract

Sustainable water management in smart cities refers to combined and intelligent management of urban water supply, distribution, wastewater, reuse and stormwater systems to guarantee the security of resources in the long term, protect the environment and resiliency of the city. This paper’s purpose is to map and assess the body of international literature about machine learning (ML)-based frameworks in sustainable water management in smart city settings. The study area comprises 2,474 documents that were published in 2020–2025 and are concerned with the intersection of water management, sustainability, and the use of ML. The methodology presupposes scientometric approach based on Scopus indexed publications and bibliometric analysis and visualization by Biblioshiny, VOSviewer and Origin Pro as the tools to evaluate the trends in publications, their thematic development, networks of collaborators, and contributors. The results show that it is a fast-growing area of research with an annual growth rate of 13.48, high citation impact, and a robust cooperation on the global level, mainly driven by China, India, the United Kingdom, and Australia. Significant research areas are wastewater treatment, sustainability, adsorption-based processes, nutrient removal, and increased use of ML in monitoring, forecasting and optimizing the systems. The findings indicate a definite shift in the isolated, technology-focused research, to integrated, system-level, sustainability-focused smart water systems, as well as identify gaps in research on governance, equity and policy integration. Overall, the research highlights the strategic significance of ML-based solutions to the development of sustainable water management of smart cities and provides a general research roadmap of the future interdisciplinary research.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Vijaya Prakash
AU  - G. Shyamala
AU  - D. Sushma Kumari
PY  - 2026
DA  - 2026/06/16
TI  - Machine Learning-Driven Framework for Sustainable Water Management in Smart Cities
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 423
EP  - 434
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_42
DO  - 10.2991/978-94-6239-693-7_42
ID  - Prakash2026
ER  -