About This Architecture

Real-time water quality monitoring system combining fluorescence biosensors, heavy metal ISE sensors, and environmental probes with edge AI/ML inference. Raw sensor data flows from ESP32/Arduino microcontrollers through WiFi/IoT connectivity to a hybrid AI/ML pipeline that fuses physics-based kinetic modeling with Random Forest classification. The pipeline extracts kinetic features (lag time, reaction rate, ratiometric values), applies noise filtering and normalization, then outputs bacterial concentration, heavy metal levels, and water safety classification (Safe/Moderate/Unsafe) to a real-time dashboard with alert triggers. This architecture demonstrates how to embed domain knowledge (fluorescence kinetics) alongside machine learning for accurate, interpretable water quality predictions at the edge.

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How do you build a real-time water quality monitoring system that combines IoT sensors with AI/ML for accurate bacterial and heavy metal detection?

This diagram shows a complete architecture where fluorescence biosensors, heavy metal ISE sensors, and environmental probes feed data through an ESP32/Arduino microcontroller to a hybrid AI/ML pipeline. The pipeline combines physics-based kinetic modeling with Random Forest classification, extracting features like lag time and reaction rate, then outputs water safety classifications and alerts in