Making waves: Generative artificial intelligence in water distribution networks: Opportunities and challenges

Abstract

Water distribution networks (WDNs) face increasing challenges from aging infrastructure, population growth, and climate change, necessitating innovative technological solutions. This study examines the integration of Generative Artificial Intelligence (GenAI) in WDNs, including both conventional and reclaimed water systems. Through a comprehensive analysis of current literature and emerging applications, the study identifies key opportunities in near-future applications focusing on enhancing information retrieval through advanced document processing, improving water quality management via real-time monitoring and visualization, implementing predictive maintenance strategies through pattern recognition, and optimizing real-time operational control through adaptive algorithms. Results also demonstrate that GenAI can transform WDN operations through advanced visualization, scenario generation, and adaptive optimization capabilities, particularly in far-future applications such as demand forecasting, emergency response, and network design optimization. The analysis reveals significant challenges, including data quality and availability issues, particularly in non-English speaking regions, scalability constraints in large-scale networks, the critical need for water professionals with hybrid expertise in both traditional engineering and AI systems, and complex regulatory requirements that vary significantly across the globe. The study also explores unique applications in reclaimed WDNs, particularly in quality control, treatment optimization, and stakeholder engagement. These findings provide water utilities, policymakers, and researchers with valuable insights for implementing GenAI technologies while balancing technological advancement with human expertise and social responsibility.