IoT WSN-Based Water Quality Monitoring System with AI Sensors¶
Membranes & Advanced Filtration • Est. CapEx: Low-cost / Locally fabricated
Technical Overview¶
A smart framework integrating IoT, wireless sensor networks, and AI machine learning models for real-time water quality monitoring. It uses physical sensors for standard parameters and AI models to predict indicators like E. coli without dedicated electronic sensors, targeting water treatment plants and municipal systems.
Key Specifications & Engineering Parameters¶
| Parameter | Specification |
|---|---|
| Category | Membranes & Advanced Filtration |
| Capital Cost | Low-cost / Locally fabricated |
| Primary Mechanism | Sensor nodes measure parameters (pH, conductivity, turbidity, etc.) and transmit data wirelessly to a central platform. AI algorithms, such as AdaBoost, are trained on historical sensor data to predict non-sensor indicators (e.g., E. coli) by correlating them with physically measured variables, enabling proactive contamination alerts. |
| Target Scale | Household / Village Cluster |
| Standard Lifespan | 10 - 20 Years |
Cross-Reference & Interactive Tools¶
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