AI Driven Predictive Maintenance in Water and Wastewater Systems: Enhancing Efficiency, Reliability, and Sustainability

This paper investigates the potential of Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve predictive maintenance in water and wastewater systems. The objective is to reduce system failures, optimize operational costs, and enhance reliability by shifting from reactive to proactive maintenance strategies. Methods employed include a literature review of current AI applications (such as anomaly detection, supervised learning models, and IoT integration) and analysis of real-world case studies that showcase the feasibility of these solutions. Outcomes demonstrate key benefits such as early warning systems, predictive accuracy, optimized maintenance scheduling, and cost reduction. The study also discusses implementation challenges and future research directions, including data quality concerns, workforce readiness, and ethical considerations. The findings suggest AI-driven maintenance offers a sustainable, efficient pathway to enhance infrastructure resilience in the water sector.