Unlocking the Potential of AI in the Water Sector

The growing challenges in water quality (WQ) management driven by population growth, industrial activity, and climate change, pose serious risks to public health, food security, and environmental sustainability. Traditional monitoring methods often fall short in dynamic or resource-limited contexts. This chapter explores how artificial intelligence (AI), in conjunction with the Internet of Things (IoT), cloud computing, and advanced analytics, is transforming water management across critical sectors such as surface and river waters, drinking water systems, aquaculture systems, and wastewater treatment. A particular focus is placed on the integration of continual learning (CL), a branch of machine learning (ML) that enables systems to adapt to new data without forgetting prior knowledge. By addressing issues like catastrophic forgetting, CL enhances the robustness of predictive models over time. The chapter presents two use cases of modern aquaculture and the enhancement of wastewater quality forecasting through a CL approach. Comparative insights highlight the relevance of CL techniques such as experience replay (ER), adaptive experience replay (AER), and learning without forgetting based on knowledge distillation (LwF-KD). Overall, this chapter argues that AI and CL offer sustainable, real-time, and adaptive solutions to the complex demands of modern WQ management, particularly in decentralized or water-scarce regions.