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Water quality monitoring using machine learning and IoT: a review

Water remains one of the most essential natural resources. With the ever-increasing population, the demand for water across various sectors, including agriculture, industry, and power, as well as the growing prevalence of pollution, has led to a significant strain on water supplies. The availability...

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Main Authors: Hasan, Tahsin Fuad, Kabbashi, Nassereldeen Ahmed, Saleh, Tanveer, Alam, Md. Zahangir, Abd Wahab, Mohd Firdaus, Nour, Abdurahman Hamid
Format: Article
Language:English
Published: IIUM Press, IIUM 2024
Subjects:
TD169 Environmental protection
Online Access:http://irep.iium.edu.my/117619/7/117619_Water%20quality%20monitoring.pdf
http://irep.iium.edu.my/117619/
https://journals.iium.edu.my/bnrej/index.php/bnrej
https://doi.org/10.31436/cnrej.v8i2.100
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spelling my.iium.irep.1176192025-01-10T02:26:11Z http://irep.iium.edu.my/117619/ Water quality monitoring using machine learning and IoT: a review Hasan, Tahsin Fuad Kabbashi, Nassereldeen Ahmed Saleh, Tanveer Alam, Md. Zahangir Abd Wahab, Mohd Firdaus Nour, Abdurahman Hamid TD169 Environmental protection Water remains one of the most essential natural resources. With the ever-increasing population, the demand for water across various sectors, including agriculture, industry, and power, as well as the growing prevalence of pollution, has led to a significant strain on water supplies. The availability of fresh and usable water is becoming increasingly limited, making quality monitoring and analysis crucial for sustainable use and environmental protection. Traditional water quality monitoring techniques involve manual sampling, testing, and investigation, which may not always be reliable and are often inefficient in providing early warnings of water quality deterioration. However, with the emergence of machine learning (ML) and Internet of Things (IoT) technologies, the process of water quality monitoring and analysis has become more efficient, accurate, and cost-effective. ML algorithms can analyze large volumes of water quality data, enabling data-centric approaches to designing, supervising, simulating, assessing, and refining various water treatment and management systems. This review paper provides an overview of the past and current applications of machine learning and IoT in water quality monitoring and analysis. Long-term cost savings can be seen in different ways as reduced labor costs, lower operational costs, early detection and intervention prevent costly repairs and emergencies, minimized infrastructure costs, distributed IoT sensors reduce the need for extensive physical infrastructure, optimized resource allocation and efficiency improvements with IoT and Machine Learning in water quality monitoring can be highlighted in the following points, real-time monitoring: immediate data analysis allows for prompt adjustments and decision-making, enhanced accuracy, advanced sensors and algorithms improve data precision and reliability, scalability, systems can be easily expanded or adapted to meet evolving needs, predictive maintenance, automated systems proactively address issues before they escalate, reducing manual oversight. The paper explores various ML algorithms, including supervised and unsupervised learning and deep learning, along with their applications, and discusses the use of IoT sensors for real-time monitoring of water quality parameters such as pH, dissolved oxygen, temperature, and turbidity. IIUM Press, IIUM 2024-12-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/117619/7/117619_Water%20quality%20monitoring.pdf Hasan, Tahsin Fuad and Kabbashi, Nassereldeen Ahmed and Saleh, Tanveer and Alam, Md. Zahangir and Abd Wahab, Mohd Firdaus and Nour, Abdurahman Hamid (2024) Water quality monitoring using machine learning and IoT: a review. Chemical And Natural Resources Engineering Journal, 8 (2). pp. 32-54. E-ISSN 2637-0719 https://journals.iium.edu.my/bnrej/index.php/bnrej https://doi.org/10.31436/cnrej.v8i2.100
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TD169 Environmental protection
spellingShingle TD169 Environmental protection
Hasan, Tahsin Fuad
Kabbashi, Nassereldeen Ahmed
Saleh, Tanveer
Alam, Md. Zahangir
Abd Wahab, Mohd Firdaus
Nour, Abdurahman Hamid
Water quality monitoring using machine learning and IoT: a review
description Water remains one of the most essential natural resources. With the ever-increasing population, the demand for water across various sectors, including agriculture, industry, and power, as well as the growing prevalence of pollution, has led to a significant strain on water supplies. The availability of fresh and usable water is becoming increasingly limited, making quality monitoring and analysis crucial for sustainable use and environmental protection. Traditional water quality monitoring techniques involve manual sampling, testing, and investigation, which may not always be reliable and are often inefficient in providing early warnings of water quality deterioration. However, with the emergence of machine learning (ML) and Internet of Things (IoT) technologies, the process of water quality monitoring and analysis has become more efficient, accurate, and cost-effective. ML algorithms can analyze large volumes of water quality data, enabling data-centric approaches to designing, supervising, simulating, assessing, and refining various water treatment and management systems. This review paper provides an overview of the past and current applications of machine learning and IoT in water quality monitoring and analysis. Long-term cost savings can be seen in different ways as reduced labor costs, lower operational costs, early detection and intervention prevent costly repairs and emergencies, minimized infrastructure costs, distributed IoT sensors reduce the need for extensive physical infrastructure, optimized resource allocation and efficiency improvements with IoT and Machine Learning in water quality monitoring can be highlighted in the following points, real-time monitoring: immediate data analysis allows for prompt adjustments and decision-making, enhanced accuracy, advanced sensors and algorithms improve data precision and reliability, scalability, systems can be easily expanded or adapted to meet evolving needs, predictive maintenance, automated systems proactively address issues before they escalate, reducing manual oversight. The paper explores various ML algorithms, including supervised and unsupervised learning and deep learning, along with their applications, and discusses the use of IoT sensors for real-time monitoring of water quality parameters such as pH, dissolved oxygen, temperature, and turbidity.
format Article
author Hasan, Tahsin Fuad
Kabbashi, Nassereldeen Ahmed
Saleh, Tanveer
Alam, Md. Zahangir
Abd Wahab, Mohd Firdaus
Nour, Abdurahman Hamid
author_facet Hasan, Tahsin Fuad
Kabbashi, Nassereldeen Ahmed
Saleh, Tanveer
Alam, Md. Zahangir
Abd Wahab, Mohd Firdaus
Nour, Abdurahman Hamid
author_sort Hasan, Tahsin Fuad
title Water quality monitoring using machine learning and IoT: a review
title_short Water quality monitoring using machine learning and IoT: a review
title_full Water quality monitoring using machine learning and IoT: a review
title_fullStr Water quality monitoring using machine learning and IoT: a review
title_full_unstemmed Water quality monitoring using machine learning and IoT: a review
title_sort water quality monitoring using machine learning and iot: a review
publisher IIUM Press, IIUM
publishDate 2024
url http://irep.iium.edu.my/117619/7/117619_Water%20quality%20monitoring.pdf
http://irep.iium.edu.my/117619/
https://journals.iium.edu.my/bnrej/index.php/bnrej
https://doi.org/10.31436/cnrej.v8i2.100
_version_ 1821105156719116288
score 13.23648

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