Conference
Intelligent water quality management through machine learning: A random forest approach for sustainable systems.
| Τίτλος: | Intelligent water quality management through machine learning: A random forest approach for sustainable systems. |
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| 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] |
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| Βάση Δεδομένων: | Complementary Index |
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