Intelligence Layer
How WasteWise AI predicts and prioritizes civic waste management to assist human decision makers.
The MVP uses a tailored rules-engine and K-Nearest Neighbors (KNN) machine learning model to calculate real-time overflow risk for every zone in Abuja.
Unlike reactive legacy systems that wait for bins to overflow, our algorithm identifies pressure buildup by analyzing the frequency of user reports, urgency signals, and historical missed pickups.
- Urgency Level: Resident subjective assessment.
- Waste Type Severity: Organic waste decays faster than recycling.
- Temporal Density: Time elapsed since the last service.
- Geographic Clustering: Repeated complaints in similar zones automatically boost priority scores.
How Risk is Calculated
Age of the pending reports
Type of waste reported
Volume of reports in area
User declared status
Subjective citizen alert severity
Organic rot vs benign materials (Time + Type)
Volume of complaints in the exact geographic cluster
AI dynamically triages this score out of 100 to alert dispatch managers instantly.
This system uses predictive scoring and prioritization logic for demonstration purposes. It is designed to support human decision making, not replace it.
The risk scores provided in the dashboard are suggestive. Dispatch supervisors always retain final authority on truck routing and resource allocation. WasteWise AI does not process personally sensitive biometric data or use facial recognition.