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Climate·G Space·Evidence-backed gain·Published 2026-07-21

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These…

TRV-2026-0468Peer-reviewedPermanent record — cite & verify
Advances in machine learning and IoT for water quality monitoring: A comprehensive review

In-network processing on low-cost IoT nodes for maritime surveillance by Belding, Andrew R.. Public domain

The quick read

Published March 1, 2024 in Heliyon, this peer-reviewed review examines the current state of water quality monitoring using IoT wireless technologies and machine learning. It describes IoT enabling real-time collection and ML enabling accurate predictions that inform decisions to identify at-risk areas and prevent contamination.

The work matters because it connects AI-driven prediction to protection of a vital everyday resource, showing how continuous monitoring could support timely interventions. What remains uncertain is how well these integrated IoT-ML approaches perform outside reviewed studies, as the authors note ongoing challenges and open research questions for integration.

Main points
  • The paper is a comprehensive review focused on IoT wireless technologies and ML techniques for water quality monitoring.
  • IoT technologies examined include Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, RFID, cellular networks, and Bluetooth.
  • ML approaches discussed include both supervised and unsupervised algorithms for analyzing and interpreting collected water quality data.
Gain

IoT-enabled real-time data collection combined with machine learning analysis enables accurate predictions of water quality to support safeguarding decisions and preventive measures against contamination.

The rundown

The review surveys how IoT enables real-time data collection from diverse sources for efficient water quality monitoring, and how that data is then analyzed with ML.

It details specific wireless options studied for WQM including LpWAN, Wi-Fi, Zigbee, RFID, cellular networks, and Bluetooth, and notes use of both supervised and unsupervised ML algorithms.

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