Water management through artificial intelligence capabilities

Document Type : Original Article

Authors

1 1- Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Tehran, Iran.

2 Faculty of Civil, Water and Environmental engineering, Shahid Beheshti University, Tehran, Iran

Abstract

The water sector faces escalating challenges driven by increasing demand, climate change, urban expansion, and the limitations of traditional management methods. The complexity of water quality dynamics, reliance on operator experience, and the inadequacy of linear models highlight the need for more advanced technologies. Artificial intelligence (AI), with its capacity for large-scale data analysis, real-time monitoring, predictive modeling, and process optimization, has emerged as a transformative tool in water and wastewater management. This study reviews key AI applications in decision-support systems, reservoir control, shortage prediction, treatment optimization, leak detection, consumption management, and smart irrigation. It also examines AI’s role in improving drinking water quality, reducing human error, enhancing energy efficiency, and strengthening system resilience under climate stress. Despite its advantages, AI deployment faces challenges such as data scarcity, technological constraints, and limited interdisciplinary expertise. The paper concludes with recommendations for strengthening infrastructure and expanding AI integration in the water sector.

Keywords

References

Alam, G., Ihsanullah, I., Naushad, M. and Sillanpää, M., (2022). "Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects". Chemical Engineering Journal, 427, p.130011.
Alshehri, M., Bhardwaj, A., Kumar, M., Mishra, S. and Gyani, J., (2021). "Cloud and IoT based smart architecture for desalination water treatment". Environmental research, 195, p.110812.
Arabameri, A., Saha, S., Mukherjee, K., Blaschke, T., Chen, W., Ngo, P.T.T. and Band, S.S., (2020). "Modeling spatial flood using novel ensemble artificial intelligence approaches in northern Iran". Remote Sensing, 12(20), p.3423.
Ben Ayed, R. and Hanana, M., (2021). "Artificial intelligence to improve the food and agriculture sector". Journal of Food Quality, 2021(1), p.5584754.
Boulouard, Z., Ouaissa, M., Ouaissa, M., Siddiqui, F., Almutiq, M. and Krichen, M., (2022). "An integrated artificial intelligence of things environment for river flood prevention". Sensors, 22(23), p.9485.
Chen, C., Hu, Y., Karuppiah, M. and Kumar, P.M., (2021). "Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies". Sustainable Energy Technologies and Assessments, 47, p.101358.
Chowdhury, S. and Karanfil, T., (2024). "Applications of Artificial Intelligence (AI) in Drinking Water Treatment Processes: Possibilities". Chemosphere, p.141958.
Chowdhury, S. and Husain, T., (2020). "Reducing the dimension of water quality parameters in source water: An assessment through multivariate analysis on the data from 441 supply systems". Journal of Environmental Management, 274, p.111202.
D’Amore, G., Di Vaio, A., Balsalobre-Lorente, D. and Boccia, F., (2022). "Artificial intelligence in the water–energy–food model: a holistic approach towards sustainable development goals". Sustainability, 14(2), p.867.
Dawood, T., Elwakil, E., Novoa, H.M. and Delgado, J.F.G., (2020). "Artificial intelligence for the modeling of water pipes deterioration mechanisms". Automation in Construction, 120, p.103398.
Ding, S., Li, H., Su, C., Yu, J. and Jin, F., (2013). "Evolutionary artificial neural networks: a review". Artificial Intelligence Review, 39, pp.251-260.
Doorn, N., (2021). "Artificial intelligence in the water domain: Opportunities for responsible use". Science of the Total Environment, 755, p.142561.
Fameso, F.O., Ndambuki, J.M., Kupolati, W.K. and Snyman, J., (2024). "On the Development of State-of-the-Art Computational Decision Support Systems for Efficient Water Quality Management: Prospects and Opportunities in a Climate Changing World". Air, Soil and Water Research, 17, p.11786221241259949.
Fan, X., Zhang, X. and Yu, X.B., (2021). "Machine learning model and strategy for fast and accurate detection of leaks in water supply network". Journal of Infrastructure Preservation and Resilience, 2, pp.1-21.
Ghandehari, S., Montazer-Rahmati, M.M. and Asghari, M., (2011). "A comparison between semi-theoretical and empirical modeling of cross-flow microfiltration using ANN". Desalination, 277(1-3), pp.348-355.
Gorlapalli, A., Kallakuri, S., Sreekanth, P.D., Patil, R., Bandumula, N., Ondrasek, G., Admala, M., Gireesh, C., Anantha, M.S., Parmar, B. and Yadav, B.K., (2022). "Characterization and prediction of water stress using time series and artificial intelligence models". Sustainability, 14(11), p.6690.
Gunasekaran, K. and Boopathi, S., (2023). "Artificial intelligence in water treatments and water resource assessments". In Artificial Intelligence Applications in Water Treatment and Water Resource Management (pp. 71-98). IGI Global.
Hmoud Al-Adhaileh, M. and Waselallah Alsaade, F., (2021). "Modelling and prediction of water quality by using artificial intelligence". Sustainability, 13(8), p.4259.
Hu, X., Han, Y., Yu, B., Geng, Z. and Fan, J., (2021). "Novel leakage detection and water loss management of urban water supply network using multiscale neural networks". Journal of Cleaner Production, 278, p.123611.
Ibrahim, K.S.M.H., Huang, Y.F., Ahmed, A.N., Koo, C.H. and El-Shafie, A., (2022). "A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting". Alexandria Engineering Journal, 61(1), pp.279-303.
Ighalo, J.O., Adeniyi, A.G. and Marques, G., (2021). "Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis". Modeling Earth Systems and Environment, 7(2), pp.669-681.
Ismail, W., Niknejad, N., Bahari, M., Hendradi, R., Zaizi, N.J.M. and Zulkifli, M.Z., (2021). "Water treatment and artificial intelligence techniques: a systematic literature review research". Environmental Science and Pollution Research, pp.1-19.
Jan, F., Min-Allah, N. and Düştegör, D., (2021). "Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications". Water, 13(13), p.1729.
Jawad, J., Hawari, A.H. and Zaidi, S.J., (2021). "Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review". Chemical Engineering Journal, 419, p.129540.
Jenny, H., Alonso, E.G., Wang, Y. and Minguez, R., (2020). "Using artificial intelligence for smart water management systems". https://doi.org/10.22617/brf200191-2
Kamyab, H., Khademi, T., Chelliapan, S., SaberiKamarposhti, M., Rezania, S., Yusuf, M., Farajnezhad, M., Abbas, M., Jeon, B.H. and Ahn, Y., (2023). "The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management". Results in Engineering, p.101566.
Kavya, M., Mathew, A., Shekar, P.R. and Sarwesh, P., (2023). "Short term water demand forecast modelling using artificial intelligence for smart water management". Sustainable Cities and Society, 95, p.104610.
Krishnan, S.R., Nallakaruppan, M.K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J. and Sethuraman, S., (2022). "Smart water resource management using Artificial Intelligence—A review". Sustainability, 14(20), p.13384.
Kristian, A., Goh, T.S., Ramadan, A., Erica, A. and Sihotang, S.V., (2024). "Application of ai in optimizing energy and resource management: Effectiveness of deep learning models". International Transactions on Artificial Intelligence, 2(2), pp.99-105.
Li, L., Rong, S., Wang, R. and Yu, S., (2021). "Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review". Chemical Engineering Journal, 405, p.126673.
Lowe, M., Qin, R. and Mao, X., (2022). "A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring". Water, 14(9), p.1384.
 Malviya, A. and Jaspal, D., (2021). "Artificial intelligence as an upcoming technology in wastewater treatment: a comprehensive review". Environmental Technology Reviews, 10(1), pp.177-187.
Maurya, B.M., Yadav, N., Amudha, T., Satheeshkumar, J., Sangeetha, A., Parthasarathy, V., Iyer, M., Yadav, M.K. and Vellingiri, B., (2024). "Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges". Chemosphere, 353, p.141474.
Mishra, R.K., (2023). "Fresh water availability and its global challenge". British Journal of Multidisciplinary and Advanced Studies, 4(3), pp.1-78.
Niu, W.J. and Feng, Z.K., (2021). "Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management". Sustainable Cities and Society, 64, p.102562.
Nourani, V., Elkiran, G. and Abba, S.I., (2018). "Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach". Water Science and Technology, 78(10), pp.2064-2076.
Obaideen, K., Yousef, B.A., AlMallahi, M.N., Tan, Y.C., Mahmoud, M., Jaber, H. and Ramadan, M., (2022). "An overview of smart irrigation systems using IoT". Energy Nexus, 7, p.100124.
Pham, B.T., Luu, C., Van Phong, T., Nguyen, H.D., Van Le, H., Tran, T.Q., Ta, H.T. and Prakash, I., (2021). "Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam". Journal of Hydrology, 592, p.125815.
Ramadhan, A.J., Ali, A.M. and Kareem, H.K., (2020). "Smart water-quality monitoring system based on enabled real-time internet of things". J. Eng. Sci. Technol, 15(6), pp.3514-3527.
Ray, P.P., (2023). "Leveraging deep learning and language models in revolutionizing water resource management, research, and policy making: A case for ChatGPT". ACS ES&T Water, 3(8), pp.1984-1986.
Sánchez, J.M., Rodríguez, J.P. and Espitia, H.E., (2020). "Review of artificial intelligence applied in decision-making processes in agricultural public policy". Processes, 8(11), p.1374.
Talaviya, T., Shah, D., Patel, N., Yagnik, H. and Shah, M., (2020). "Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides". Artificial Intelligence in Agriculture, 4, pp.58-73.
Toryila, T.M., Obiora, O.C., Jiya, V. and Mustapha, M., (2023). "An overview of the application of artificial intelligence in water engineering". International Journal of Engineering Science and Application, 7(1), pp.22-31.
Vanijjirattikhan, R., Khomsay, S., Kitbutrawat, N., Khomsay, K., Supakchukul, U., Udomsuk, S., Suwatthikul, J., Oumtrakul, N. and Anusart, K., (2022). "AI-based acoustic leak detection in water distribution systems". Results in Engineering, 15, p.100557.
Wang, D. and Xiang, H., (2019). "Composite control of post-chlorine dosage during drinking water treatment". IEEE Access, 7, pp.27893-27898.
Wu, J., Cao, M., Tong, D., Finkelstein, Z. and Hoek, E.M., (2021). " A critical review of point-of-use drinking water treatment in the United States". NPJ Clean Water, 4(1), p.40.
Xiang, X., Li, Q., Khan, S. and Khalaf, O.I., (2021). "Urban water resource management for sustainable environment planning using artificial intelligence techniques". Environmental Impact Assessment Review, 86, p.106515.
Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J. and Yang, Y., (2020). "Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse". Process Safety and Environmental Protection, 133, pp.169-182.
Zhong, S., Zhang, K., Bagheri, M., Burken, J.G., Gu, A., Li, B., Ma, X., Marrone, B.L., Ren, Z.J., Schrier, J. and Shi, W., (2021). "Machine learning: new ideas and tools in environmental science and engineering". Environmental science & technology, 55(19), pp.12741-12754.
Interdisciplinary Journal of Civil Engineering
Volume 1, Issue 3 - Serial Number 3
September 2025
Pages 282-293

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