If a study reports multiple experiments with the same (M,P), we count it once at the study level to avoid overweighting prolific papers; results are robust to counting at the experiment level.
* Adamowski J, Chan H (2015) Water demand forecasting: review of soft computing methods. Environ Model Softw 60:1-9. https://doi.org/10.1007/s10661-017-6030-3
Article Google Scholar
* Ahmed Osman AI, AlDahoul N, Chong KL, Huang YF, Ng JL, Elshafie A, … Ahmed AN (2025) A review on machine learning models for drought monitoring and forecasting. Clim Risk Manag 50:100758. https://doi.org/10.1016/j.crm.2025.100758
* Akinsoji AH, Adelodun B, Adeyi Q, Salau RA, Odey G, Choi KS (2024) Integrating machine learning models with comprehensive data strategies and optimization techniques to enhance flood prediction accuracy: a review. Water Resour Manage 38:4735-4761. https://doi.org/10.1007/s11269-024-03885-x
Article Google Scholar
* Atitallah SB, Driss M, Boulila W, Ghézala HB (2020) Leveraging deep learning and IoT big data analytics to support the smart cities development: review and future directions. Comput Sci Rev 38:100303. https://doi.org/10.1016/j.cosrev.2020.100303
Article Google Scholar
* Bibri SE (2018) The IoT for smart sustainable cities of the future: an analytical framework for sensor-based big data applications for environmental sustainability. Sustain Cities Soc 38:230-253. https://doi.org/10.1016/j.scs.2017.12.034
Article Google Scholar
* Bischl B, Casalicchio G, Das T, Feurer M, Fischer S, Gijsbers P, … Wever M (2025) Openml: insights from 10 years and more than a thousand papers. Patterns 6(7):101317. https://doi.org/10.1016/j.patter.2025.101317
* Cina E, Elbasi E, Elmazi G, AlArnaout Z (2025) The role of ai in predictive modelling for sustainable urban development: challenges and opportunities. Sustainability 17(11). https://doi.org/10.3390/su17115148
* Fu G, Jin Y, Sun S, Yuan Z, Butler D (2022) The role of deep learning in urban water management: a critical review. Water Res 223:118973. https://doi.org/10.1016/j.watres.2022.118973
Article CAS Google Scholar
* Garzón A, Kapelan Z, Langeveld J, Taormina R (2022) Machine learning-based surrogate modeling for urban water networks: review and future research directions. Water Resour Res 58(5):e2021WR031808. https://doi.org/10.1029/2021WR031808
Article Google Scholar
* Ghannam S, Hussain F (2024) Short-term water demand forecasting: a review. Australas J Water Resour 1-19. https://doi.org/10.1080/13241583.2024.2350102
* Gonzalez-Cebollada C, Djordjević S, Savić D (2020) Urban water demand modeling: review of concepts, methods, and organizing principles. Water Res 173:115519. https://doi.org/10.1029/2010WR009624
Article Google Scholar
* Herath H, Mittal M (2022) Adoption of artificial intelligence in smart cities: a comprehensive review. Int J Inform Manag Data Insights 2(1):100076. https://doi.org/10.1016/j.jjimei.2022.100076
Article Google Scholar
* Infant SS, Vickram S, Saravanan A, Mathan Muthu CM, Yuarajan D (2025) Explainable artificial intelligence for sustainable urban water systems engineering. Results Eng 25:104349. https://doi.org/10.1016/j.rineng.2025.104349
Article Google Scholar
* Islam MJ, Salekin SU, Anzum A, Zaman N, Khan AAA, Sarkar D, … Hossain MT (2024) Machine learning-driven water quality index prediction: enhancing accuracy with gradient boosting and explainable ai for sustainable water monitoring. Appl Agric Sci 2(1)https://doi.org/10.25163/agriculture.2110031
* Kim B (2020) Moving forward with digital disruption: what big data, IoT, synthetic biology, AI, blockchain, and platform businesses mean to libraries. Libr Technol Rep 56(2):1-32. https://doi.org/10.5860/ltr.56n2
Article Google Scholar
* Krippendorff K (2018) Content analysis: an introduction to its methodology, 4th edn. SAGE Publications, Los Angeles, CA
Google Scholar
* Kumar V, Kedam N, Kisi O, Alsulamy S, Khedher KM, Salem MA (2024) A comparative study of machine learning models for daily and weekly rainfall forecasting. Water Resour Manage 39:271-290. https://doi.org/10.1007/s11269-024-03969-8
Article Google Scholar
* Liu J, Yang H, Gosling SN, Kummu M, Flörke M, Pfister S, … Oki T (2017) Water scarcity assessments in the past, present, and future. Earth’s Future 5(6):545-559. https://doi.org/10.1002/2016ef000518
* Lumbreras S, Ciller P (2025) Interpretable optimization: why and how we should explain optimization models. Appl Sci 15(10):5732. https://doi.org/10.3390/app15105732
Article CAS Google Scholar
* Makumbura RK, Mampitiya L, Rathnayake N, Meddage D, Henna S, Dang TL, … Rathnayake U (2024) Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like Shapley additive explanations (SHAP) for interpreting the black-box nature. Results Eng 23:102831. https://doi.org/10.1016/j.rineng.2024.102831
* Marzi G, Balzano M, Caputo A, Pellegrini MM (2025) Guidelines for bibliometric-systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. Int J Manag Rev 27(1):81-103. https://doi.org/10.1111/ijmr.12381
Article Google Scholar
* Moeinzadeh H, Yong K-T, Withana A (2024) A critical analysis of parameter choices in water quality assessment. Water Res 258:121777. https://doi.org/10.1016/j.watres.2024.121777
Article CAS Google Scholar
* Mosavi A, Ozturk P, Chau K-W (2018) A review of the hybrid artificial intelligence and optimization modeling of hydrological streamflow forecasting. Eng Appl Comput Fluid Mech 12(1):1-19. https://doi.org/10.1016/j.aej.2021.04.100
Article Google Scholar
* Nickerson RC, Varshney U, Muntermann J (2013) A method for taxonomy development and its application in information systems. Eur J Inf Syst 22(3):336-359. https://doi.org/10.1057/ejis.2012.26
Article Google Scholar
* Niknam A, Zare HK, Hosseininasab H, Mostafaeipour A, Herrera M (2022) A critical review of short-term water demand forecasting tools — what method should i use? Sustainability 14(9):5412. https://doi.org/10.3390/su14095412
Article Google Scholar
* Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1186/s13643-021-01626-4
Article Google Scholar
* Rafiei Sardooi E, Bazrafshan O, Jamshidi S (2023) Modeling the water security in a watershed using the water footprint concept and water scarcity indicators. Water Supply 24(1):235-253. https://doi.org/10.2166/ws.2023.323
Article CAS Google Scholar
* Rehamnia I, Mahdavi-Meymand A (2025) Advancing reservoir water level predictions: evaluating conventional, ensemble and integrated swarm machine learning approaches. Water Resour Manage 39:779-794. https://doi.org/10.1007/s11269-024-03990-x
Article Google Scholar
* Sarıman G, Keçebaş A (2026) Global renewable energy forecasting using hybrid ml/dl models: economic and geospatial insights. Energy Policy 208:114929. https://doi.org/10.1016/j.enpol.2025.114929
Article Google Scholar
* Sattler BJ, Friesen J, Tundis A, Pelz PF (2023) Modeling and validation of residential water demand in agent-based models: a systematic literature review. Water 15(3):579. https://doi.org/10.3390/w15030579
Article Google Scholar
* Schmitz V, Vandeghen R, Erpicum S, Pirotton M, Archambeau P, Dewals B (2025) How do decision-tree-based machine learning techniques compare to hybrid approaches for predicting fluvial dike breach discharge? Water Resour Manage 39:7793-7809. https://doi.org/10.1007/s11269-025-04318-z
Article Google Scholar
* Seelen LM, Flaim G, Jennings E, De Senerpont Domis LN (2019) Saving water for the future: public awareness of water usage and water quality. J Environ Manage 242:246-257. https://doi.org/10.1016/j.jenvman.2019.04.047
Article Google Scholar
* Shen C (2018) A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour Res 54(11):8558-8593. https://doi.org/10.1029/2018WR022643
Article Google Scholar
* Shen D, Varis O (2000) World water vision: balancing thoughts after The Hague. Ambio 523-525. https://doi.org/10.1639/0044-7447(2000)0290523:wwvbta2.0.co;2
* Sit M, Demir I, Seker DZ (2021) Applications of machine learning to water resources management: a review of present status and future opportunities. J Hydrol 603:126543. https://doi.org/10.1016/j.jclepro.2024.140715
Article Google Scholar
* Siyal A, Gerbens-Leenes P, Vaca-Jiménez S (2023) Freshwater competition among agricultural, industrial, and municipal sectors in a water-scarce country. lessons of Pakistan’s fifty-year development of freshwater consumption for other water-scarce countries. Water Resour Ind 29:100206. https://doi.org/10.1016/j.wri.2023.100206
Article Google Scholar
* The Government of Chile (2015) Cuidemos el agua: cifras y recomendaciones. (https://www.gob.cl/noticias/cuidemos-el-agua-cifras-y-recomendaciones. Accessed on 02 Feb 2023)
* Tzanakakis VA, Paranychianakis NV, Angelakis AN (2020) Water supply and water scarcity. Water 12(9):2347. https://doi.org/10.3390/w12092347
Article Google Scholar
* Velasquez S, Nguyen D (2025) Machine learning methods for weather forecasting: recent progress and open challenges. Atmosphere 16(1):1-27. https://doi.org/10.3390/atmos16010055
Article CAS Google Scholar
* Yang R, Hu J, Li Z, Mu J, Yu T, Xia J, … Xiong H (2024) Interpretable machine learning for weather and climate prediction: a review. Atmos Environ 338:120797. https://doi.org/10.1016/j.atmosenv.2024.120797
* Zubaidi SL, Ortega-Martorell S, Kot P, Alkhaddar RM, Abdellatif M, Gharghan SK, … Hashim K (2020) A method for predicting long-term municipal water demands under climate change. Water Resour Manage 34(3):1265-1279. https://doi.org/10.1007/s11269-020-02500-z
