Proactive ML-Enabled Predictive UAVs Communication Network Design for Galamsey Surveillance

Illegal artisanal gold mining (“galamsey”) degrades Ghana’s rivers and forests. UAV monitoring is promising but remains reactive due to miscalibrated perception and unreliable links under canopy. We design Galamsey-911, a proactive system that (i) calibrates a multi-modal Severity Index (post-hoc temperature scaling, small ensembles), (ii) uses an SLA-aware LTE→Mesh→SATCOM stack with ACK timers and rapid failover, and (iii) forecasts 24–72 h hotspots through ConvLSTM, spatiotemporal GNNs, and TFT. Perception is simulated in AirSim; networking in ns-3 [39,19]. Calibrated perception achieved AUROC ≥ 0.90 with ECE ≤ 0.07. The dispatcher met p95 latency ≤ 30 s with ≥ 95% delivery across failovers. Forecasts delivered ≥ 30% lead-time gains, raising patrol coverage +36%/battery-hour. End-to-end detection-to-dispatch latency fell 28% versus a baseline. Combining calibrated ML, SLA-aware multi-path communications, and hotspot forecasting improves timeliness and robustness for galamsey surveillance and generalizes to disaster response and ecological protection.