AI-Driven Water Quality Prediction and Aquatic Pollution Monitoring Using Machine Learning Algorithms

Abstract

Water quality degradation is a major environmental concern affecting aquatic ecosystems, fisheries, public health, and sustainable water resource management. Conventional water quality monitoring generally depends on laboratory analysis and periodic field sampling, which may not provide immediate information about pollution events. Recent advances in artificial intelligence, machine learning, and Internet of Things based sensing provide new opportunities for real-time water quality assessment and aquatic pollution monitoring. This paper proposes an AI-driven framework for water quality prediction and aquatic pollution monitoring using machine learning algorithms. The proposed system collects water quality parameters such as pH, dissolved oxygen, temperature, turbidity, total dissolved solids, electrical conductivity, nitrate, phosphate, biochemical oxygen demand, and chemical oxygen demand from aquatic monitoring stations or sensor nodes. The collected data are preprocessed through missing value handling, outlier removal, normalization, and feature selection. The water quality index is computed to obtain an overall quality score, while machine learning algorithms including Random Forest, Support Vector Machine, Artificial Neural Network, Long Short-Term Memory, and K-Means clustering are used for prediction, classification, forecasting, and pollution-zone grouping. The framework produces water quality class, pollution risk level, and early warning output for aquatic management. The performance analysis shows that the hybrid AI model provides reliable accuracy, improved prediction performance, and practical decision support for environmental monitoring. The proposed approach is suitable for rivers, lakes, aquaculture ponds, reservoirs, coastal water bodies, and smart environmental surveillance systems.