Water Quality Evaluation using Agentic AI

Water is the basic necessity of mankind and evaluation of water quality is highly significant for both environmental sustainability and public health. Traditional approaches applied for water quality analysis lack adaptability and interpretability and are unable to provide real-time assessments and informed decisions. In this paper, a unique framework has been presented to perform water quality analysis based on Agentic AI. The proposed approach combines Rule-Based WQI Calculations and Machine Learning methods, with multi-agent systems. The proposed system is composed of specialized agents, which include data agents, planning agents, analysis agents, knowledge agents, and coordination agents all working together to create a more intelligent and reliable system. The proposed framework was evaluated using a dataset containing 5,200 water quality samples collected from multiple monitoring locations, consisting of physicochemical parameters such as dissolved oxygen, turbidity, pH, temperature, and total dissolved solids (TDS). A stratified 80:20 train-test split along with 10-fold cross-validation was employed to ensure robustness and generalization of the model. Statistical analysis demonstrated that the proposed framework significantly outperformed traditional WQI and stand-alone machine learning models with an average improvement of 13.4% in classification accuracy and 11.2% in F1-score (p