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The effective monitoring of water quality is essential for ensuring the sustainable management of water resources and safeguarding environmental and human health. This study explores the technical factors involved in real-time monitoring of water quality, focusing on sensing technology, information and communication technologies (ICT), data transmission systems, and advanced data analysis techniques such as machine learning. The integration of these technologies has the potential to revolutionize the monitoring of water quality, supplying precise and timely information to support decision-making and resource management. The study begins by analyzing sensing technologies for measuring water quality, discussing in situ sensors for physical and chemical parameters, optical remote sensors, and electrochemical detection of algal toxins. It then delves into the technical factors affecting real-time monitoring, including data transmission systems and wireless sensor technology. Challenges such as interoperability, power consumption, and data security are discussed in the context of ICT integration. Furthermore, the study explores advanced data analysis techniques for water quality assessment, emphasizing the role of machine learning algorithms in analyzing massive datasets and improving prediction accuracy. The potential of machine learning, including deep learning methods, for addressing complex water quality challenges is highlighted. The findings suggest that the integration of ICT, sensing technology, and advanced data analysis holds promise for enhancing real-time water quality monitoring. However, challenges such as data interoperability, sensor reliability, and algorithm accuracy must be critically assessed in order to reach the maximum potential of these technologies. Future research directions include optimizing sensor networks, improving data transmission efficiency, and refining machine learning models for water quality analysis. Overall, this study contributes to the understanding of technical factors in real-time water quality monitoring and informs strategies for improving water resource management practices.

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Advancements in Real-Time Water Quality Monitoring: Integrating Sensing Technology, ICT, and Data-Driven Approaches

  • 章节

  • 发表日期：

    2025年

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  • Suman Dutta,
  • Debashis Paul,
  • Malini Roy Choudhury,
  • Sumanta Das

摘要

The effective monitoring of water quality is essential for ensuring the sustainable management of water resources and safeguarding environmental and human health. This study explores the technical factors involved in real-time monitoring of water quality, focusing on sensing technology, information and communication technologies (ICT), data transmission systems, and advanced data analysis techniques such as machine learning. The integration of these technologies has the potential to revolutionize the monitoring of water quality, supplying precise and timely information to support decision-making and resource management. The study begins by analyzing sensing technologies for measuring water quality, discussing in situ sensors for physical and chemical parameters, optical remote sensors, and electrochemical detection of algal toxins. It then delves into the technical factors affecting real-time monitoring, including data transmission systems and wireless sensor technology. Challenges such as interoperability, power consumption, and data security are discussed in the context of ICT integration. Furthermore, the study explores advanced data analysis techniques for water quality assessment, emphasizing the role of machine learning algorithms in analyzing massive datasets and improving prediction accuracy. The potential of machine learning, including deep learning methods, for addressing complex water quality challenges is highlighted. The findings suggest that the integration of ICT, sensing technology, and advanced data analysis holds promise for enhancing real-time water quality monitoring. However, challenges such as data interoperability, sensor reliability, and algorithm accuracy must be critically assessed in order to reach the maximum potential of these technologies. Future research directions include optimizing sensor networks, improving data transmission efficiency, and refining machine learning models for water quality analysis. Overall, this study contributes to the understanding of technical factors in real-time water quality monitoring and informs strategies for improving water resource management practices.