Rockfall incidents in open pit mining environments pose a significant threat to human life, equipment, and operational continuity. Early detection of unstable slope conditions is essential to reduce accidents and improve mine safety. This paper presents RockSense, an Internet of Things (IoT)-based rockfall prediction and alert system designed for real-time monitoring of slope stability in open pit mines. The system integrates multiple sensors— vibration, rain, tilt, and ultrasonic distance sensors—interfaced with an Arduino Uno microcontroller to continuously monitor environmental and geotechnical parameters. Sensor data is transmitted in real time via Bluetooth to a backend server, where it is stored and analyzed using a Random Forest machine learning model to predict rockfall risk levels. Based on the predicted risk category—Safe, Warning, or Danger—alerts are displayed on a live monitoring dashboard, and critical warnings are sent to nearby personnel through SMS notifications. The system demonstrates an average classification F1-score of 96.2%, enabling early detection of hazardous conditions and contributing to improved safety and risk management in mining operations.
Sanjay et al. (Sat,) studied this question.