Artificial Intelligence In Water Quality Monitoring For Sustainable Resource Management

Abstract

The growing demand for clean and safe water, coupled with the increasing pressures of urbanization, industrialization, and climate change, has made efficient water quality monitoring a critical component of sustainable resource management. Traditional water monitoring methods, while effective, often suffer from limitations such as high costs, labor intensiveness, and delayed data interpretation. This study explores the integration of artificial intelligence (AI) techniques—including machine learning (ML) and deep learning models—for the prediction, classification, and real-time assessment of water quality parameters such as pH, turbidity, dissolved oxygen, biochemical oxygen demand (BOD), and chemical oxygen demand (COD). Various AI algorithms, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Long Short-Term Memory (LSTM) networks, are trained and evaluated using historical water quality datasets from diverse geographic regions. The results demonstrate that AI-based models can accurately predict water quality indices (WQI), outperforming conventional statistical methods in both accuracy and adaptability. Moreover, the integration of AI with Internet of Things (IoT)-based sensor networks enables continuous and automated monitoring, offering significant advantages in scalability and responsiveness. This study highlights the potential of AI as a transformative tool in environmental monitoring and underscores its critical role in promoting sustainable water resource management and policy decision-making.