SCENTINEL

SCENTINEL addresses how odour impacts urban quality of life and operational efficiency in waste and wastewater management. Municipalities currently plan collections and maintenance by fixed schedules or fullness levels, missing odour-driven signals that better reflect citizen experience and sanitary risk. Odour hotspots—especially from organic waste, plastics, or sewers—arise unpredictably due to heat, humidity, and microclimate differences. Late or unnecessary interventions increase costs, complaints, and asset corrosion. By leveraging AI-driven predictive analytics and multi-source spatial data, the system transforms traditional reactive maintenance into a proactive and preventive model. Data streams from strategically deployed IoT sensors will feed into a GIS-based digital twin of the sewer network. Using advanced algorithms such as Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), Long Short-Term Memory (LSTM) models, and ensemble learning, the platform will provide real-time risk assessment and decision support for utilities and municipal authorities.