Open Access Open Access  Restricted Access Subscription Access

RockSense: An IoT-Based Rockfall Prediction and Alert System for Open Pit Mines

Anap Komal Sanjay, Doke Vikrant Sachin, Ekhande Sagar Ramnath, Mane Arti Balasaheb, Mrs.J.V. Thorat

Abstract


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.


Full Text:

PDF

References


Corominas J, Moya J, Ledesma A, Lloret A. Rockfall hazard and risk assessment. Engineering Geology 2008;102(3–4): 125–140.

Jaboyedoff M, Oppikofer T, Abellán A, Derron MH, Loye A, Metzger R, Pedrazzini A. Use of LiDAR in landslide investigations: A review. Natural Hazards 2012;61(1):5–28.

Hungr O, Leroueil S, Picarelli L. The Varnes classification of landslide types, an update. Landslides 2014;11(2):167–194.

Abellán A, Jaboyedoff M, Oppikofer T, Vilaplana JM. Detection of rockfalls using terrestrial laser scanning. Natural Hazards and Earth System Sciences 2010;10(12):2617–2630.

Saha AK, Ghosh PK. IoT-based real-time monitoring system for landslide prediction. International Journal of Advanced Computer Science and Applications 2019;10(6):287–293.

Bhattacharya RK, Sarkar S, Das S. Wireless sensor network based landslide detection system. Procedia Computer Science 2015;58:243–250.

Breiman L. Random forests. Machine Learning 2001;45(1):5– 32.

Merghadi A, Yunus AP, Dou J, Whiteley J, ThienBao P, Tien Bui D, Abderrahmane B. Machine learning methods for landslide susceptibility mapping: A comparative study. Engineering Geology 2020;268:105–119.

Goh ATC. Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics 2007;34(5):410–421.

Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci

S. Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 2010;114(3):129–142.


Refbacks

  • There are currently no refbacks.