Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review
Traditional water quality monitoring methods face significant limitations, including delayed data acquisition, high operational costs, and inadequate spatial and temporal resolution, which hinder timely responses to contamination events. This systematic review addresses these gaps by evaluating the...
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| Format: | Article |
| Language: | English |
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Engiscience Publisher
2025-06-01
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| Series: | Journal of Studies in Science and Engineering |
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| Online Access: | https://engiscience.com/index.php/josse/article/view/633 |
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| author | Mahmoud Saleh Al-Khafaji Layth Abdulameer Muthanna M. A. AL-Shammari Najah M. L. Al Maimuri Anmar Dulaimi Dhiya Al‑Jumeily |
| author_facet | Mahmoud Saleh Al-Khafaji Layth Abdulameer Muthanna M. A. AL-Shammari Najah M. L. Al Maimuri Anmar Dulaimi Dhiya Al‑Jumeily |
| author_sort | Mahmoud Saleh Al-Khafaji |
| collection | DOAJ |
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Traditional water quality monitoring methods face significant limitations, including delayed data acquisition, high operational costs, and inadequate spatial and temporal resolution, which hinder timely responses to contamination events. This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. By synthesizing 1,032 studies (2011–2025), we demonstrate that AI-driven systems achieve 94% accuracy in prediction and reduce field sampling costs by 60% through Landsat 8 satellite integration. Our analysis reveals a 13-fold increase in AI adoption since 2011, with innovations such as adaptive neuro-fuzzy inference systems (ANFIS) and deep neural networks (DNNs) facilitating real-time anomaly detection and contamination forecasting. The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. These findings offer policymakers actionable insights, advocating for hybrid frameworks that integrate AI with existing infrastructure to bridge urban-rural disparities in water management.
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| format | Article |
| id | doaj-art-68f6ea17d74549afb6f1ba10e2d1dd5c |
| institution | Matheson Library |
| issn | 2789-634X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Engiscience Publisher |
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| series | Journal of Studies in Science and Engineering |
| spelling | doaj-art-68f6ea17d74549afb6f1ba10e2d1dd5c2025-06-30T19:45:39ZengEngiscience PublisherJournal of Studies in Science and Engineering2789-634X2025-06-015110.53898/josse2025528Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic ReviewMahmoud Saleh Al-Khafaji0Layth Abdulameer1Muthanna M. A. AL-Shammari2Najah M. L. Al Maimuri3Anmar Dulaimi4Dhiya Al‑Jumeily5Department of Water Resources Engineering, College of Engineering, University of Baghdad, Baghdad, 10071, IraqPetroleum Engineering Department, College of Engineering, University of Kerbala, Kerbala, 56001, IraqPetroleum Engineering Department, College of Engineering, University of Kerbala, Kerbala, 56001, IraqBuilding and Construction Technologies Engineering Department, College of Engineering and Engineering Technologies, Al-Mustaqbal University, Babylon, Hillah, 51001, IraqDepartment of Civil Engineering, College of Engineering, University of Kerbala, Karbala, 56001, Iraq Department of Civil Engineering, College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, IraqSchool of Computer Science and Mathematics, Faculty of Engineering and Technology, Liverpool John Moores Uni-versity, Liverpool, L33AF, UK Traditional water quality monitoring methods face significant limitations, including delayed data acquisition, high operational costs, and inadequate spatial and temporal resolution, which hinder timely responses to contamination events. This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. By synthesizing 1,032 studies (2011–2025), we demonstrate that AI-driven systems achieve 94% accuracy in prediction and reduce field sampling costs by 60% through Landsat 8 satellite integration. Our analysis reveals a 13-fold increase in AI adoption since 2011, with innovations such as adaptive neuro-fuzzy inference systems (ANFIS) and deep neural networks (DNNs) facilitating real-time anomaly detection and contamination forecasting. The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. These findings offer policymakers actionable insights, advocating for hybrid frameworks that integrate AI with existing infrastructure to bridge urban-rural disparities in water management. https://engiscience.com/index.php/josse/article/view/633Artificial IntelligenceMachine LearningReal-time monitoringSmart water managementWater quality monitoring |
| spellingShingle | Mahmoud Saleh Al-Khafaji Layth Abdulameer Muthanna M. A. AL-Shammari Najah M. L. Al Maimuri Anmar Dulaimi Dhiya Al‑Jumeily Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review Journal of Studies in Science and Engineering Artificial Intelligence Machine Learning Real-time monitoring Smart water management Water quality monitoring |
| title | Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review |
| title_full | Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review |
| title_fullStr | Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review |
| title_full_unstemmed | Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review |
| title_short | Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review |
| title_sort | revolutionizing water quality monitoring with artificial intelligence a systematic review |
| topic | Artificial Intelligence Machine Learning Real-time monitoring Smart water management Water quality monitoring |
| url | https://engiscience.com/index.php/josse/article/view/633 |
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