Advantages and Challenges of AI-Driven Water Quality Monitoring

Water quality monitoring is essential for protecting human health and ecosystems, as well as supporting broader sustainable goals. Traditional monitoring techniques often consume significant time and resources, leading to inefficiencies that may contribute to environmental degradation. Water quality monitoring with artificial intelligence (AI) provides an innovative approach through real-time, high-precision monitoring. In this chapter, AI-based water quality assessment methods such as machine learning, deep learning, and predictive analytics will be discussed. These tools enable real-time detection of emerging contaminants like methane, per- and polyfluoroalkyl substances (PFAS), microplastics, and others, allowing for efficient responses. The capacity of AI to handle large datasets enhances resource optimization, reducing greenhouse gas emissions in water management systems. This chapter explores various AI-driven water quality monitoring techniques, including machine learning, deep learning, and predictive analytics. These methods offer faster, more precise responses to water quality issues. The ability of AI to process large datasets improves resource management and reduces greenhouse gas emissions. However, challenges such as the need for comprehensive datasets remain. Future advancements like edge computing and advanced AI models are also discussed, emphasizing AI’s role in achieving net-zero emissions.