EventsThe 9th International Electronic Conference on Water Sciences
Published
This submission belongs to the session S7. Remote Sensing, Artificial Intelligence and New Technologies in Water Sciences of the event The 9th International Electronic Conference on Water Sciences
Published date
06 Nov, 2025
Academic Editor
author-avatarNikiforos Samarinas
Citation
Ali Tavallali, Next-Generation Water Management: Integrating Generative and Agentic AI for Enhanced Efficiency and Resilience, in Proceedings of The 9th International Electronic Conference on Water Sciences, 11 November–14 November 2025, MDPI: Basel, Switzerland
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Next-Generation Water Management: Integrating Generative and Agentic AI for Enhanced Efficiency and Resilience

1. Department of Construction Science and Organizational Leadership, Purdue University Northwest, Hammond, 46323, USA, USA
Abstract

The management of water distribution and wastewater treatment facilities is confronting unprecedented challenges, including aging infrastructure, climate-induced stressors, and increasing operational complexities. While traditional automation has improved efficiency, it often lacks the foresight and adaptability required for dynamic system management. This abstract introduces a transformative paradigm integrating two advanced artificial intelligence frameworks, Generative AI and Agentic AI, to create self-adapting, resilient water systems.

Generative AI is leveraged for its powerful predictive and simulation capabilities. By training on vast datasets of historical and real-time operational data, it can generate highly realistic digital twin simulations to forecast system behavior, predict component failures, and model the impact of various environmental or demand scenarios. Furthermore, generative models can design optimized operational schedules and novel infrastructure configurations that enhance efficiency and minimize energy consumption.

Complementing this foresight, Agentic AI provides the capacity for autonomous action and real-time decision-making. Deployed as a network of intelligent agents, this framework can independently control physical assets such as pumps, valves, and chemical dosing systems. These agents interpret the predictive insights from generative models to proactively adjust operations, autonomously manage maintenance tasks, and coordinate rapid, localized responses to disruptions like pipe bursts or contaminant ingress.

The synergy between generative foresight and agentic action creates a robust, closed-loop management system that not only optimizes day-to-day operations but also fundamentally enhances the long-term resilience and sustainability of water infrastructure. This integrated approach promises significant reductions in operational costs, minimization of water loss, and a higher standard of water quality and security for communities.

Keywords
Generative AI
Agentic AI
Smart Water Management
Water Distribution Systems
Wastewater Treatment
Digital Twin
Autonomous Systems
Infrastructure Resilience
Poster
Ali Tavallali.pdf
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