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Intelligent water quality management through machine learning: A random forest approach for sustainable systems.

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Τίτλος: Intelligent water quality management through machine learning: A random forest approach for sustainable systems.
Συγγραφείς: Zrouri, Amira, Farissi, Ilhame El
Πηγή: AIP Conference Proceedings; 2026, Vol. 3495 Issue 1, p1-8, 8p
Θεματικοί όροι: Water quality monitoring, Random forest algorithms, Software libraries (Computer programming), Water quality management, Sustainability, Environmental management, Public health, Machine learning
Περίληψη: Water quality is crucial for both public health and environmental sustainability. In this article, we present an intelligent water quality monitoring system based on the Random Forest algorithm and the React library. The aim of our system is to offer an innovative solution to effectively anticipate and monitor water quality, an essential element for public health and environmental protection. Using the Random Forest method for data classification and React for the user interface, we have developed a robust and easy-to-use platform. This platform allows users to easily access accurate water quality data and take preventive measures if necessary. We also expose in depth the methods of data collection and processing, as well as the results of the system evaluated using different quality measurements. By combining sophisticated methods with contemporary web technologies, our system represents a major breakthrough in water quality monitoring, contributing to the preservation of the environment and public health. [ABSTRACT FROM AUTHOR]
Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Intelligent water quality management through machine learning: A random forest approach for sustainable systems.
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  Data: <searchLink fieldCode="DE" term="%22Water+quality+monitoring%22">Water quality monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+management%22">Water quality management</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+management%22">Environmental management</searchLink><br /><searchLink fieldCode="DE" term="%22Public+health%22">Public health</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Water quality is crucial for both public health and environmental sustainability. In this article, we present an intelligent water quality monitoring system based on the Random Forest algorithm and the React library. The aim of our system is to offer an innovative solution to effectively anticipate and monitor water quality, an essential element for public health and environmental protection. Using the Random Forest method for data classification and React for the user interface, we have developed a robust and easy-to-use platform. This platform allows users to easily access accurate water quality data and take preventive measures if necessary. We also expose in depth the methods of data collection and processing, as well as the results of the system evaluated using different quality measurements. By combining sophisticated methods with contemporary web technologies, our system represents a major breakthrough in water quality monitoring, contributing to the preservation of the environment and public health. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1063/12.0044628
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        Text: English
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        PageCount: 8
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      – SubjectFull: Water quality monitoring
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Software libraries (Computer programming)
        Type: general
      – SubjectFull: Water quality management
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      – SubjectFull: Sustainability
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      – SubjectFull: Environmental management
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      – SubjectFull: Public health
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      – SubjectFull: Machine learning
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              Text: 2026
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