EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 81 (2026) ITM Web Conf., 81 (2026) 01027 References
Open Access
Issue
ITM Web Conf.
Volume 81, 2026
International Conference on Emerging Technologies for Multidisciplinary Innovation and Sustainability (ETMIS 2025)
Article Number 01027
Number of page(s) 15
DOI https://doi.org/10.1051/itmconf/20268101027
Published online 23 January 2026
  1. Alsumaiei, A. A. (2025). Interprétable machine learning framework for managing shallow water table rise in urban aquifers. Hydrology Research, 56(5), 397–418. [Google Scholar]
  2. Bolorinos, J., Josa, L., & Gual, M. A. (2020). Anomaly detection in water consumption time series using machine learning techniques. Procedia Computer Science, 176, 2901–2910. [Google Scholar]
  3. Carvalho, T. M. N., de Brito, P. S. D., & de Araüjo, J. C. (2021). Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil. Water Resources Management, 35(2), 527–543. [Google Scholar]
  4. Chen, S. & Lu, J. (2022). Machine Learning Based Prediction of Water Demand in Megacities: A Case Study of Beijing. Journal of Information Systems Engineering & Management, 7(2), 1–12. [Google Scholar]
  5. Chen, Y., Huang, C., Wu, Y., & Li, Z. (2020). Machine learning in water quality prediction: A review. Water Science and Technology, 82(10), 2269–2286. [Google Scholar]
  6. Cominola, A., Spang, M., & Giuliani, M. (2018). Data-driven user segmentation and profiling for enhanced water conservation policies. Journal of Cleaner Production, 171, 1481–1491. [Google Scholar]
  7. Danandeh Mehr, A., & Moghaddam Nia, A. (2025). Editorial: Emerging machine learning and optimization techniques in urban water systems. AQUA - Water Infrastructure, Ecosystems and Society, 74(10). [Google Scholar]
  8. Dogo, E. (2019). Smart water systems: The role oftechnology and engineering in optimizing urban water resources. Journal of Information Systems Engineering & Management, 10(21s), 833–846. [Google Scholar]
  9. Firat, M., Gungor, O. B., & Sen, T. K. (2008). Time series forecasting of daily water consumption using artificial neural networks. Water Resources Management, 22(4), 859–871. [Google Scholar]
  10. Guo, K., Huang, C., Li, J., & Wang, Y. (2022). Optimal pump scheduling for water distribution systems using deep reinforcement learning. Water Research, 220, 118671. [CrossRef] [PubMed] [Google Scholar]
  11. Kim, H., Jung, K. J., Lee, S., & Jeong, E. H. (2024b). Rapid response to pressure variations in water distribution networks through machine learning-enhanced data acquisition. AQUA - Water Infrastructure, Ecosystems and Society, 73(7), 1358–1371. Kim, H., Jung, K. J., Lee, S., & Jeong, E. H. (2024a). Enhanced gate-valve failure detection in water distribution networks using ML and pressure data. AQUA - Water Infrastructure, Ecosystems and Society, 73(5), 969–979. [Google Scholar]
  12. Koutra, S., & Ioakimidis, C. S. (2023). Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges. Land, 12(1), 83. [Google Scholar]
  13. Kwon, S., & Kim, J. H. (2021). Machine Learning and Urban Drainage Systems: State-of-the-Art Review. Water, 13(24), 3617. [Google Scholar]
  14. Lee, C., & Derrible, S. (2020). Predicting water consumption using machine learning: An evaluation of model accuracy and the role of data availability. Sustainable Cities and Society, 53, 101895. [Google Scholar]
  15. Li, J., & Sansalone, J. J. (2022). Implementing machine learning to optimize the cost-benefit of urban water clarifier geometrics. Water Research, 216, 118314. [Google Scholar]
  16. Ma, S., Li, Y., & Chen, J. (2024). Real-time Control of Urban Water Systems Using Machine Learning (Doctoral dissertation, University of Exeter). [Google Scholar]
  17. Maier, H. R., Taghikhah, F., Nabavi, E., Razavi, S., Gupta, H. V., Wu, W., Radford, D. A. G., & Huang, J. (2025). Editorial: Emerging machine learning and optimization techniques in urban water systems. Hydrology Research, 56(5), 393–396. [Google Scholar]
  18. Samoili, S., Cobo, M. J., Gomez, E., De Prato, G., Martinez-Plumed, F., & Delipetrev, B. (2020). AI Watch: Defining and classifying artificial intelligence. European Commission. [Google Scholar]
  19. S., & Gholampour, A. (2024). Stochastic differential equations for modeling wastewater treatment plant dynamics. AQUA - Water Infrastructure, Ecosystems and Society, 75(10), 1682–1693. [Google Scholar]
  20. Sharififard, S., Afshar, M. H., & Kim, H. (2024). Estimating creep function coefficients in viscoelastic pipes by integrating XGBoost regression with transient hydraulic simulations. AQUA - Water Infrastructure, Ecosystems and Society, 75(10), 1735–1750. [Google Scholar]
  21. Su, P. (2020). Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities. Journal of Information Systems Engineering & Management, 5(2), 1–10. [Google Scholar]
  22. Villarin, M. C., & Rodrigez-Galiano, V. (2019). Spatio-temporal analysis of urban water demand using machine learning techniques. Water Resources Management, 55(12), 4353–1369. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.