A comprehensive review of AI-driven water-quality monitoring and prediction: advances, challenges, and future directions

Abstract

Water quality has emerged as a critical global concern that requires advanced monitoring and management strategies. Traditional water-quality assessment methods predominantly rely on laboratory-oriented analysis that are time consuming, expensive, and are often labor-intensive. This study presents a thorough overview of the paradigm shift from conventional lab analysis towards intelligent and automated, AI-based assessment and monitoring frameworks. The survey systematically explains the use of machine learning (ML), deep learning (DL), and hybrid models to analyze complicated multidimensional and nonlinear water parameters. This study evaluates predictive architectures ranging from traditional regression-based models to advanced ensembles and deep neural networks (CNNs, ANNs, and RNNs) integrated with IoT computing technologies for continuous water quality monitoring. Enhancing models’ interpretability and transparency through Explainable Artificial Intelligence methods like SHAP and LIME are analyzed. This survey addresses existing challenges like data scarcity, interpretability of the model, and computational complexity. Self-attention-based architectures, generative AI, and edge computing used to improve the robustness of future water-quality management systems are also assessed. An overall structured research perspective on AI-driven water-quality assessment with methodological gaps with future scope are established in this survey.