The Role of Machine Learning in Water Quality Assessment: Current Applications and Future Scope

Water quality is vital for human health, ecosystems, industries, and agriculture. However, increasing contamination and pollution over recent decades have posed significant challenges to maintaining clean water sources. Effective monitoring is essential for safeguarding public health, protecting the environment, and ensuring sustainable water management. Artificial Intelligence (AI), particularly machine learning (ML), offers powerful tools for water quality assessment, classification, and prediction. With the rapid expansion of aquatic environmental data, ML has become indispensable for analyzing complex, nonlinear patterns that traditional models struggle to address. Unlike conventional approaches, ML-driven models can efficiently process vast datasets, improving the accuracy of water quality monitoring and forecasting. ML has been widely applied in water research, enhancing the development, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. These models assist in detecting contamination patterns, predicting pollution levels, and improving decision-making processes. Furthermore, ML-driven approaches contribute to water pollution control, quality enhancement, and watershed ecosystem security management. This review explores the applications of ML in assessing water quality across diverse environments, including surface water, groundwater, drinking water, sewage, and seawater. We highlight key case studies demonstrating the effectiveness of ML in water quality management and discuss emerging opportunities for its future implementation. By leveraging AI and ML, researchers and policymakers can develop smarter, data-driven strategies for ensuring cleaner and safer water resources.