IoT-AI Framework for Smart Reverse Osmosis Water Purification and Distribution

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.

By Choudhury, Pandey, MishraNatural Sciences and Applied TechnologySep 30, 2026

Score████░░░░░░4.4

Key numbers

95.7 % precision in identifying distribution

VerdictWorth a reader's time today.

Read the original

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.

Siddharth Choudhury, Anish Pandey, Ruby Mishra

The editor's rubric

Heuristic review

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	16%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░ 3	20%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	10%	A new combination of known ideas.
Trajectory	███░░ 3	14%	A clear path to scale.
Stakes	██░░░ 2	20%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (programmable); gains (outperforms); verification (experimental validation); scale (efficient); stakes (climate).

How the score was computed

rank-2026-09-29

Score████░░░░░░4.4

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Merit: 5.4 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.6 / 10; Shrunk toward the desk prior by editor confidence (40%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 97%; Half-life decay since publication.

Citations0 (reference 15, via openalex, Sep 30, 2026, 11:05 UTC)
Field-weighted citation impact0 (reference 3, via openalex, Sep 30, 2026, 11:05 UTC)

The record

Reviewed by heuristic-v2 on Sep 30, 2026, 11:05 UTC. Paper type: empirical.
Categories: Membrane Separation Technologies, Water Quality Monitoring Technologies, Water Systems and Optimization, Water Science and Technology, Environmental Science
TOP, No.6 in the Climate & Energy edition of September 30, 2026.