Predicting Oxygen Concentrations in the Dender River
Monitoring water quality is essential for protecting river ecosystems and ensuring sustainable management. Traditional methods often rely on manual sampling and delayed analysis, limiting the ability to respond quickly to environmental changes. This pilot case introduces an AI-driven decision-support system for real-time prediction and management of oxygen levels (O2) in rivers across Flanders.
IoT sensors deployed in the rivers continuously collect data on water conditions. This information is processed by AI models developed by the University of Lille, combining historical and real-time data to predict fluctuations in oxygen levels and detect potential ecological risks. The system provides public authorities and water managers with early warnings, predictive insights, and actionable recommendations for interventions such as pollution control, maintenance, and biodiversity protection.
VMM contributes real-world datasets, ecological expertise and validation, while GEOSPARC ensures clear visualization and deployment on an online platform. This integrated, data-driven approach transforms raw environmental measurements into practical intelligence, improving decision-making, operational efficiency, and ecological resilience.
By enabling proactive, real-time water management, the pilot represents a significant step forward in smart, AI-based environmental monitoring. It helps authorities safeguard river ecosystems while optimizing resources and supporting sustainable water management across the Flanders region.
