AI-Based Rockfall Prediction System

Abstract

Mining sites and steep geotechnical environments face significant risk from unexpected rockfall events, which can lead to severe injuries, infrastructure damage, financial loss, and operational downtime. Traditional inspection-based monitoring techniques often fail to provide timely warnings due to manual limitations, human error, and the dynamic nature of geological conditions. As a result, there is a growing need for an intelligent, automated, and proactive safety system that can assess ground stability and predict hazardous events in advance.

This project presents an AI-Based Rockfall Prediction System designed to forecast potential rockfall occurrences using machine learning and multi-parameter environmental data. The system analyzes key risk factors such as slope angle, terrain composition, rainfall intensity, vibration frequency, temperature variations, and soil moisture levels. By learning patterns associated with previous rockfall incidents, the model generates risk scores and early alerts, enabling mine operators to take preventive action before failures occur.

The system uses a scalable architecture that includes data preprocessing, feature extraction, model training, and prediction. Developed using Python and machine- learning frameworks, it supports flexible and real-world deployment. This AI-based approach enhances mining safety by enabling early risk prediction and data-driven decisions. It minimizes reliance on manual monitoring and can be extended to IoT sensors, drones, and real-time surveillance systems.