Harnessing Generative AI for smarter network management and optimization

The rapid evolution of Generative AI (GAI) and large language models (LLMs) drives a transformative shift in the water sector, particularly in water distribution systems. These technologies offer advanced opportunities for water utilities and researchers to enhance distribution networks' efficiency, sustainability, and resilience. This article explores the recent advancements in GAI and how they can be applied to address the challenges in managing complex water distribution systems. With their ability to process vast datasets, generate predictive insights, and simulate intricate hydraulic behaviors, these AI-driven tools enable smarter water network management, from demand prediction to anomaly detection. Water engineers in utilities often face significant time constraints and lack specialized software development expertise, limiting their ability to fully leverage digital tools for data analysis, optimization, and decision-making. GAI presents a transformative opportunity by enabling engineers to automate repetitive tasks, rapidly analyze large datasets, and generate actionable insights without requiring advanced programming skills [1]. GAI can assist in demand forecasting, anomaly detection, report generation, and decision support, streamlining workflows and improving operational efficiency. By integrating GAI into daily operations, water utilities can enhance resilience, optimize resource allocation, and support data-driven decision-making, ultimately improving service delivery and sustainability. Through interactive workshops with stakeholders, our research examines the specific barriers preventing water engineers in utilities from adopting GAI tools and seeks to develop actionable strategies for overcoming them. It identifies key challenges, including limited technological knowledge, lack of awareness of AI’s capabilities, and concerns over data security. In these workshops, we present bottom-up use cases such as converting GIS data (Figure 1) into basic EPANET [2] model (Figure 2). Additional topics ...