IoT-AI Framework for Smart Reverse Osmosis Water Purification and Distribution
Year: 2026
Type: article
Abstract: This paper presents an IoT-enabled multi-sensor monitoring framework with AI-based predictive analytics for smart reverse osmosis (RO) water purification and distribution systems. The proposed architecture integrates six flow sensors (FS1--FS6) and two temperature sensors (T1--T2) with an ESP32-based embedded platform for continuous telemetry collection and cloud-based analytics. The predictive framework comprises: (1) RO purification efficiency estimation via inlet/outlet flow ratio modeling; (2) leakage detection using mass-balance water accounting; (3) system stability assessment via rolling standard deviation; and (4) water demand forecasting using linear regression and Long Short-Term Memory (LSTM) neural networks. Experimental validation using 31,450 real-world measurements demonstrates that the LSTM model achieves superior performance (R² = 0.94, RMSE = 245 L/min) compared to linear regression (R² = 0.72, RMSE = 520 L/min). The Isolation Forest-based anomaly detection algorithm achieves 95.7\% precision in identifying distribution anomalies. The integrated framework provides a practical solution for decentralized water infrastructure management in semi-urban and institutional settings.
Institution: KIIT University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7214631330