After a rockfall, you can reconstruct exactly what happened. That analysis is worth almost nothing to the people who were standing there.
The entire value in hazard detection is on the front of the event, not the back. A crude warning a minute early can clear a slope; a flawless explanation an hour late is a report. So the problem isn't understanding a rockfall — it's seeing it coming.
The trouble is that no single sensor is trustworthy on its own. Displacement, vibration, moisture — each is noisy, each throws false alarms. But if you fused those streams into one model that weighs them together, the noise starts to cancel and a real pattern rises out of it: this combination, trending this way, means move.
That's what I engineered into an AI and IoT rockfall prediction system — a multi-sensor fusion pipeline feeding a real-time hazard model, built to raise the alarm before the slope moves rather than to explain it afterward. It was shortlisted at Smart India Hackathon 2025.
Prevention systems are judged by their lead time, not their hindsight. Design for the warning, not the write-up.
More ML and IoT projects → www.divyakush.com
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