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: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IIUM Press, IIUM
2024
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| Subjects: | |
| 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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| Summary: | 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. |
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