Predictive Analytics for Sustainable Water Quality Monitoring Using Machine Learning
Abstract
Maintaining water quality is essential for public health and environmental sustainability, yet monitoring systems often lack predictive capabilities. This paper presents a machine learning-based approach for real-time water quality monitoring, using predictive analytics to anticipate pollution events and fluctuations in water quality. By analyzing historical water quality data along with environmental factors, our model forecasts potential contamination events, enabling timely interventions. Case studies from river and coastal monitoring sites demonstrate improved prediction accuracy, allowing for proactive management of water resources and enhancing environmental protection measures.
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References
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