AI-Driven Predictive Modeling for Real-Time Water Quality Monitoring in Urban Water Supply Systems

Abstract

Ensuring safe and reliable urban water supply is a critical challenge globally, exacerbated by increasing urbanization, climate change, and aging infrastructure. Traditional water quality monitoring methods often involve manual sampling and laboratory analysis, which are time-consuming, resource-intensive, and provide only retrospective insights, hindering proactive management of contamination events. The advent of artificial intelligence (AI) and the Internet of Things (IoT) has revolutionized the field, enabling real-time, continuous monitoring and predictive modeling of water quality. This review paper explores the significant advancements in AI-driven predictive modeling for urban water supply systems. It delves into diverse AI techniques, including machine learning, deep learning, and hybrid models, employed to process vast datasets from IoT sensors, offering insights into parameters like pH, turbidity, dissolved oxygen, and conductivity. The paper highlights how these models can accurately forecast water quality parameters, detect anomalies, identify potential contamination sources, and optimize treatment processes. Furthermore, it discusses the benefits of such systems in enhancing operational efficiency, reducing health risks, and promoting sustainable water management. The integration of these technologies represents a paradigm shift towards intelligent water networks, empowering water utilities with unprecedented capabilities for real-time decision-making and rapid response to water quality fluctuations, ultimately safeguarding public health and ensuring the integrity of urban water resources.