The Integration of AI and ML in Water and Wastewater Engineering for Sustainable Infrastructure
DOI:
https://doi.org/10.32628/CSEIT2494115Keywords:
Artificial Intelligence, Machine Learning, Water Engineering, Wastewater Engineering, Smart Water, Environmental SustainabilityAbstract
Advanced digital technologies provide invaluable opportunities for sustainable water management infrastructure. AI and ML are buzzing technologies for esteem data analytics with capabilities to notice complicated patterns and correlations. These are insightful for understanding water treatment and management process elements. Leveraging AI and ML logic for analyzing data generated from water engineering treatment processes, enabling esteem plans, implementing predictive maintenance, and optimizing operational procedures are the core aspects of this study. Extracting applicable insights using complicated data trends facilitates AI and ML to develop initiative-taking water treatment strategies. ML modeling possesses capabilities to predict water demand accurately. This represents anomalies in quality due to potential system failures. Involving technical paradigms in handling wastewater treatment processes is important to mitigate the influence of climate and consumption surge. These processes are critical for managing climate/urbanization pressures on processes with future research directions. AI and ML are capable of resiliently treating wastewater with initiative-taking monitoring and implementing predictive maintenance mechanisms. This involves capabilities to oversee climate fluctuations. This paper depicts about ambivalent capabilities of implementing these technologies by mentioning practical challenges in process. This study is an attempt to explore research directions toward integrating wastewater treatment solutions with AI and ML systems. The motive of this research is to extend the aspects and investigate literature available regarding technical and data analytics potential in managing water resources. This paper underscores capabilities of AI and ML to motivate utilization of advanced technology paradigms for resilient wastewater treatment. Recommendations are included with future research motives for contributing to sustainable water management domain.
Downloads
References
Alam, G., Ihsanullah, I., Naushad, M., & Sillanpäa, M. (2022). Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chemical Engineering Journal, 427(1), 1-10. Retrieved from https://doi.org/10.1016/j.cej.2021.130011
Alprol, A. E., Mansour, A. T., Ibrahim, M. E.-D., & Ashour, M. (2024). Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective. Water, 16(2), 1-26. Retrieved from https://doi.org/10.3390/w16020314
Altowayti, W. A., Shahir, S., Eisaa, T. A., Yafooz, W. M., A. A., & Soon, C. Y. (2022). The role of conventional methods and artificial intelligence in the wastewater treatment: A Comprehensive Review. MDPI Processes, 10(9), 1-10. Retrieved from https://doi.org/10.3390/pr10091832
Alyson H. Rapp, S., Annelise M. Capener, S., & Robert B. Sowby, P. M. (2023). Adoption of Artificial Intelligence in Drinking Water Operations: A Survey of Progress in the United States. Journal of Water Resources Planning and Management, 149(7), 1-7. Retrieved from https://doi.org/10.1061/JWRMD5.WRENG-5870
Dada, M. A., Majemite, M. T., Obaigbena, A., Daraojimba, O. H., Oliha, J. S., & Nwokediegwu, Z. Q. (2024). Review of smart water management: IoT and AI in water and wastewater treatment. World Journal of Advanced Research and Reviews, 21(1), 1373–1382. Retrieved from https://doi.org/10.30574/wjarr.2024.21.1.0171
Doorn, N. (2021). Artificial Intelligence in the water domain: Opportunities for responsible use. Science of The Total Environment, 755, 142561. Science of The Total Environment, 755(1), 1-10. Retrieved from https://doi.org/10.1016/j.scitotenv.2020.142561
Exeter University. (2022, July 28). Artificial Intelligence based Detection of Pipe Bursts/Leaks and Other Events in Water Distribution Systems. Retrieved from https://researchandinnovation.co.uk: https://researchandinnovation.co.uk/artificial-intelligence-based-detection-of-pipe-bursts-leaks-and-other-events-in-water-distribution-systems/
Filho, J. V., Scortegagna, A., Vieira, A. P., & Jaskowiak, P. A. (2024). Machine learning for water demand forecasting: case study in a Brazilian coastal city. Water Practice & Technology, 19(5), 1586-1602. Retrieved from https://doi.org/10.2166/wpt.2024.096
Fu, G., Sun, S., Hoang, L., Yuan, Z., & Butler, D. (2023). Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective. Cambridge Prisms: Water, 1(14), 1-10. Retrieved from https://doi.org/10.1017/wat.2023.15
Ganthavee, V., & Trzcinski, A. P. (2024). Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review. Environmental Chemistry Letters, 22(1), 2293–2318. Retrieved from https://link.springer.com/article/10.1007/s10311-024-01748-w
Hoz, J. D., & Echeverri, E. A. (2024). Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends. MDPI Resources, 13(12), 1-10. Retrieved from https://doi.org/10.3390/resources13120171
Idrica. (2024). How AI and digital twins are changing the paradigm in treatment plants. Valencia, Spain: Idrica.
Kamyab, H., Khademi, T., Chelliapan, S., SaberiKamarposhti, M., Rezania, S., Yusuf, M., Farajnezhad, M., Abbas, M., Hun Jeon, B., & Ahn, Y. (2023). The latest innovative avenues for the utilization of artificial intelligence and Big Data Analytics in water resource management. Results in Engineering, 20, 101566. https://doi.org/10.1016/j.rineng.2023.101566
Yerra, S. (2025). Enhancing inventory management through real-time Power BI dashboards and KPI tracking.
Krishnan, S. R., Nallakaruppan, M. K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J., & Sethuraman, S. (2022). Smart Water Resource Management Using Artificial Intelligence—A Review. Sustainability, 14(20), 13384. https://doi.org/10.3390/su142013384
Lowe, M., Qin, R., & Mao, X. (2022). A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. MDPI Water, 14(9), 1-10. Retrieved from https://doi.org/10.3390/w14091384
Mathaba, M., & Bana, J. (2023). A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. Journal of Environmental Science and Health, Part A , 58(14), 1047–1060. Retrieved from https://doi.org/10.1080/10934529.2024.2309102
Najafzadeh, M., & Zeinolabedini, M. (2019). Prognostication of waste water treatment plant performance using efficient soft computing models: An environmental evaluation. Measurement, 138(1), 690-701. Retrieved from https://doi.org/10.1016/j.measurement.2019.02.014
Narayanan, D., Bhat, M., Paul, S., Khatri, N., & Saroliya, A. (2024). Artificial intelligence driven advances in wastewater treatment: Evaluating techniques for sustainability and efficacy in global facilities. Desalination and Water Treatment, 320(1), 1-10. Retrieved from https://doi.org/10.1016/j.dwt.2024.100618
Numalis. (2024, April 23). AI Innovations in Water, Sewerage, and Waste Management. Retrieved from https://numalis.com: https://numalis.com/ai-in-water-sewerage-and-waste-management/#:~:text=The%20Brembate%20WWT%20plant%20in,aeration%20costs%20while%20ensuring%20effluent
Safeer, S., Pandey, R. P., Safdar, B. R., Ahmad, I., Hasan, S. W., & Ullah, A. (2022). A review of artificial intelligence in water purification and wastewater treatment: Recent advancements. Journal of Water Process Engineering, 49(1), 1-10. Retrieved from https://doi.org/10.1016/j.jwpe.2022.102974
Sachin Dixit, & Jagdish Jangid. (2024). Asynchronous SCIM Profile for Security Event Tokens. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 1357–1371. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1935
Singh, N. K., Yadav, M., Singh, V., Padhiyar, H., Kumar, V., Bhatia, S. K., & Show, P. L. (2023). Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. Bioresource Technology, 369(1), 1-10. Retrieved from https://doi.org/10.1016/j.biortech.2022.128486
Vekaria, D., & Sinha, S. (2024). aiWATERS: an artificial intelligence framework for the water sector. AI in Civil Engineering, 3(6), 1-23. Retrieved from https://doi.org/10.1007/s43503-024-00025-7
Xiang, X., Li, Q., Khan, S., & Khalaf, O. I. (2021). Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review, 86(1), 1-23. Retrieved from https://doi.org/10.1016/j.eiar.2020.106515
Zhang, H., & Ng, C. (2024). Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments. ACS Publications, 4(3), 761–763. Retrieved from https://doi.org/10.1021/acsestwater.4c00140
Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment & Health, 1(2), 107-116. Retrieved from https://doi.org/10.1016/j.eehl.2022.06.001
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.