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

Ismail Essamlali ¹, Hasna Nhaila ¹, Mohamed El Khaili ¹

⁰Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Morocco.

Heliyon +

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March 27, 2024

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Summary

Real-time water quality monitoring is enhanced by the Internet of Things (IoT) and machine learning (ML). These technologies enable accurate predictions and informed decisions to protect water resources from contamination.

Area Of Science

Environmental Science
Computer Science
Engineering

Background

Continuous water quality monitoring is essential for public health and environmental protection.
The Internet of Things (IoT) offers real-time data acquisition capabilities for environmental sensing.
Machine learning (ML) provides powerful tools for analyzing complex environmental datasets.

Purpose Of The Study

To provide a comprehensive review of current advancements in water quality monitoring (WQM).
To focus on the integration of IoT wireless technologies and ML techniques for WQM.
To identify challenges and future research directions in this interdisciplinary field.

Main Methods

Review of existing literature on IoT wireless technologies (LpWAN, Wi-Fi, Zigbee, RFID, cellular, Bluetooth) for WQM.
Exploration of supervised and unsupervised ML algorithms applied to WQ data analysis.
Analysis of the synergy between IoT data streams and ML predictive capabilities.

Main Results

IoT enables efficient, real-time data collection for WQM.
ML techniques (supervised and unsupervised) effectively analyze WQ data for predictive insights.
Integration of IoT and ML facilitates proactive decision-making for water resource management.

Conclusions

The combination of IoT and ML represents the state-of-the-art in WQM.
Effective WQM relies on leveraging diverse IoT wireless technologies and advanced ML algorithms.
Addressing current challenges is crucial for advancing IoT-ML based WQM systems.

Keywords:

Environmental monitoring Machine learning Water quality Wireless IoT technology