Critical Review on Water Quality Analysis, Treatment and Monitoring Using Internet of Things and Artificial Intelligence for Drinking Water

Abstract

The global challenge of ensuring safe drinking water access has intensified due to population growth, industrialization, and climate change. Conventional laboratory-based water quality assessment methods, while accurate, suffer from delayed analysis, limited sampling frequency, and high operational costs. This critical review synthesizes recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies for drinking water quality analysis, treatment optimization, and real-time monitoring. IoT-enabled sensor networks facilitate continuous, high-frequency data acquisition on critical parameters—including pH (optimal 6.5–8.5), turbidity (<1–5 NTU), dissolved oxygen (>5 mg/L), and electrical conductivity (250–1500 μS/cm)—while machine learning algorithms including Random Forest, Gradient Boosting, Support Vector Machines, and Deep Neural Networks enable predictive forecasting, anomaly detection, and process optimization . The review examines the historical evolution from manual sampling to intelligent systems, critically evaluates the strong and weak points of current approaches, analyzes emerging trends such as digital twins and edge computing, and identifies persistent challenges including sensor drift, data interoperability, model interpretability, and regulatory acceptance. The findings indicate that while AI-IoT integration offers significant potential for transforming water management—demonstrating coagulant reductions of 10–22% in treatment plants and enabling early warning capabilities—substantial barriers remain for widespread operational deployment . Recommendations are provided for standardizing protocols, enhancing cross-site model generalization, and developing interpretable algorithms to bridge the gap between research innovation and practical implementation.