T.P. Sulakshna,
V.R. Pramod,
- Former Assistant Professor, Department of Civil Engineering, NSS College of Engineering, Palakkad, Kerala, India
- Associate Director, GRIP Global Pte Ltd, Far East Square, 32 Pekin Street, #05-01, Singapore 048762
Abstract
Ensuring access to safe drinking water is a critical public health challenge. Traditional water quality
assessment methods are often labor-intensive and time-consuming. Artificial intelligence offers a
promising alternative, providing rapid, accurate, and scalable solutions for monitoring and predicting
water quality. This systematic review examines the application of AI. The review highlights various AI
models, including artificial neural networks, support vector machines, decision trees, and ensemble
methods, in predicting water quality parameters and detecting contamination events. The integration
of artificial intelligence with Internet of Things devices for real-time monitoring is also discussed. Our
findings suggest that artificial intelligence-based approaches significantly enhance water quality
management, offering robust and efficient solutions for ensuring safe drinking water. The advancement
in explainable artificial intelligence, has considerably elevated the trust in using AI in drinking water
management
Keywords: Artificial intelligence, safe drinking water, water quality prediction, environmental monitoring, machine learning, IoT, explainable artificial intelligence, XAI
[This article belongs to Journal of Water Resource Engineering and Management(jowrem)]
References
1. Aldrees A, Awan HH, Javed MF, Mohamed AM. Prediction of water quality indexes with ensemble
learners: Bagging and boosting. Process Saf Environ Prot. 2022; 168: 344–361.
2. Alfwzan WF, Selim MM, Almalki AS, Alharbi IS. Water quality assessment using Bi-LSTM and
computational fluid dynamics (CFD) techniques. Alex Eng J. 2024; 97: 346–359.
3. Alves Ribeiro VH, Moritz S, Rehbach F, Reynoso-Meza G. A novel dynamic multi-criteria
ensemble selection mechanism applied to drinking water quality anomaly detection. Sci Total
Environ. 2020; 749: 142368.
4. Aslan S, Zennaro F, Furlan E, Critto A. Recurrent neural networks for water quality assessment in
complex coastal lagoon environments: A case study on the Venice Lagoon. Environ Model Softw.
2022; 154: 105403.
5. Bagheri M, Farshforoush N, Bagheri K, Shemirani AI. Applications of artificial intelligence
technologies in water environments: from basic techniques to novel tiny machine learning systems.
Process Saf Environ Prot. 2023; 180: 10–22.
6. Chee J, Cao Q, Quek C. FE-RNN: A fuzzy embedded recurrent neural network for improving
interpretability of underlying neural network. Inf Sci. 2024; 663: 120276.
7. Chen X, Liu H, Liu F, Huang T, Shen R, Deng Y, et al. Two novelty learning models developed
based on deep cascade forest to address the environmental imbalanced issues: A case study of
drinking water quality prediction. Environ Pollut. 2021; 291: 118153.
8. Dikshit A, Pradhan B. Interpretable and explainable AI (XAI) model for spatial drought prediction.
Sci Total Environ. 2021; 801: 149797.
9. Garrido-Momparler V, Peris M. Smart sensors in environmental/water quality monitoring using
IoT and cloud services. Trends Environ Anal Chem. 2022; 35.
10. Gohel P, Singh P, Mohanty M. Explainable AI: Current status and future directions. ArXiv. 2021;
[Online] Available at https://arxiv.org/abs/2107.07045
11. Hmoud Al-Adhaileh M, Waselallah Alsaade F. Modelling and prediction of water quality by using
artificial intelligence. Sustainability. 2021; 13 (8): 4259.
12. Ighalo JO, Adeniyi AG, Marques G. Artificial intelligence for surface water quality monitoring and
assessment: A systematic literature analysis. Model Earth Syst Environ. 2020; 7 (2): 669–681.
13. Ismael M, Mokhtar A, Farooq M, Lü X. Assessing drinking water quality based on physical,
chemical, and microbial parameters in the Red Sea State, Sudan using a combination of water
quality index and artificial neural network model. Groundw Sustain Dev. 2021; 14: 100612.
14. Jha BK. Cloud-based smart water quality monitoring system using IoT sensors and machine
learning. Int J Adv Trends Comput Sci Eng. 2020; 9 (3): 3403–3409.
15. John TJ, Nagaraj R. Prediction of floods using improved PCA with one-dimensional convolutional
neural network. Int J Intell Netw. 2023; 4: 122–129.
16. Leong WC, Bahadori A, Zhang J, Ahmad Z. Prediction of water quality index (WQI) using support
vector machine (SVM) and least square-support vector machine (LS-SVM). Int J River Basin
Manag. 2019; 1–8.
17. Luo W, Huang L, Shu J, Feng H, Guo W, Xia K, et al. Predicting water quality in municipal water
management systems using a hybrid deep learning model. Eng Appl Artif Intell. 2024; 133: 108420.
18. Mallick J, Alqadhi S, Hang HT, Alsubih M. Interpreting optimised data-driven solution with
explainable artificial intelligence (XAI) for water quality assessment for better decision-making in
pollution management. Environ Sci Pollut Res Int. 2024; 31.
19. Mia MY, Haque ME, Jannat JN, Islam MS. Analysis of self-organizing maps and explainable
artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor
region. Sci Total Environ. 2023; 904: 166927.
20. Mohseni U, Pande CB, Pal SC, Alshehri F. Prediction of Weighted Arithmetic Water Quality Index
for urban water quality using ensemble machine learning model. Chemosphere. 2024; 352: 141393.
21. Najah A, El-Shafie A, Karim OA, Jaafar O, El-Shafie AH. An application of different artificial
intelligences techniques for water quality prediction. Int J Phys Sci. 2011; 6 (22): 5298–5308.
22. Nallakaruppan MK, Gangadevi E, Shri ML, Balusamy B, Bhattacharya S, Selvarajan S. Reliable
water quality prediction and parametric analysis using explainable AI models. Sci Rep. 2024; 14
(1): 7520.
23. Narita K, Matsui Y, Matsushita T, Shirasaki N. Screening priority pesticides for drinking water
quality regulation and monitoring by machine learning: Analysis of factors affecting detectability.
J Environ Manag. 2023; 326: 116738.
24. Ortiz-Lopez C, Bouchard C, Rodriguez MJ. Ensemble machine learning using hydrometeorological
information to improve modeling of quality parameter of raw water supplying treatment plants. J
Environ Manag. 2024; 362: 121378.
25. Park J, Lee WH, Kim K, Park CY, Lee SH, Heo TY. Interpretation of ensemble learning to predict
water quality using explainable artificial intelligence. Sci Total Environ. 2022; 832: 155070.
26. Pasika S, Gandla ST. Smart water quality monitoring system with cost-effective using IoT. Heliyon.
2020; 6 (7): e04096.
27. Poursaeid M, Poursaeed AH, Shabanlou S. Water quality fluctuations prediction and Debi
estimation based on stochastic optimized weighted ensemble learning machine. Process Saf
Environ Prot. 2024; 188: 1160–1174.
28. Price HD, Adams EA, Nkwanda PD, Mkandawire TW, Quilliam RS. Daily changes in household
water access and quality in urban slums undermine global safe water monitoring programmes. Int
J Hyg Environ Health. 2021; 231: 113632.
29. Rana R, Kalia A, Boora A, Alfaisal FM, Alharbi RS, Berwal P, et al. Artificial intelligence for
surface water quality evaluation, monitoring and assessment. Water. 2023; 15 (22): 3919.
30. Ransom KM, Nolan BT, Stackelberg PE, Belitz K, Fram MS. Machine learning predictions of
nitrate in groundwater used for drinking supply in the conterminous United States. Sci Total
Environ. 2021; 807: 151065.
31. Sarkar SK, Talukdar S, Rahman A, Shahfahad, Roy SK. Groundwater potentiality mapping using
ensemble machine learning algorithms for sustainable groundwater management. Front Eng Built
Environ. 2021; 2 (1): 43–54.
32. Singh S, Rai S, Singh P, Mishra VK. Real-time water quality monitoring of River Ganga (India)
using internet of things. Ecol Inform. 2022; 101770.
33. Poh WK, Chia MY, Hoon KC, Huang YF, Chong WC. Applications of deep learning in water
quality management: A state-of-the-art review. J Hydrol. 2022; 128332.
34. Wu J, Wang Z, Dong J, Yao Z, Chen X, Fan H. Multi-step ahead dissolved oxygen concentration
prediction based on knowledge guided ensemble learning and explainable artificial intelligence. J
Hydrol. 2024; 636: 131297.
35. Yang L, Driscol J, Sarigai S, Wu Q, Lippitt CD, Morgan M. Towards synoptic water monitoring
systems: A review of AI methods for automating water body detection and water quality monitoring
using remote sensing. Sensors (Basel). 2022; 22 (6): 2416.
36. Zhang Q, Li Z, Zhu L, Zhang F, Sekerinski E, Han JC, et al. Real-time prediction of river chloride
concentration using ensemble learning. Environ Pollut. 2021; 291: 118116.
Journal of Water Resource Engineering and Management
| Volume | 11 |
| Issue | 02 |
| Received | July 17, 2024 |
| Accepted | July 24, 2024 |
| Published | July 26, 2024 |