Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review

Abstract

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 inter-pretability, and ethical governance. These findings offer policymakers actionable insights, advo-cating for hybrid frameworks that integrate AI with existing infrastructure to bridge urban-rural disparities in water management.