Interpretable Ensemble Model for Real-Time Water Quality Analysis Using Iot and AI Strategies

Real-time monitoring is a necessity for effective control of the deterioration of water quality brought on by pollution and environmental variables. Conventional techniques are slow, laborious, and incapable of gathering data continuously. This paper presents an IoT and AI-based water quality monitoring system that uses real-time data gathering and predictive analytics. Measurable characteristics like pH, turbidity, dissolved oxygen, and total dissolved solids (TDS) are measured with the Internet of Things sensors placed in water bodies, which sends data wirelessly to a cloud-based processing unit. The ensemble machine learning model, XGBoost, analyzes the data and forecasts trends, accurately classifies water quality, and identifies abnormalities. SHAP, an Explainable AI (XAI) technique, that enhance the model transparency and allow stakeholders to understand how decisions are made. Additionally, if water quality falls outside acceptable limits, an alert system sends real-time messages for prompt action. The proposed framework offers a proactive approach to environmental management by ensuring high accuracy, scalability, and efficiency in the monitoring of water quality.