ML-Based Rockfall Prediction System

A machine learning web app that predicts potential rockfall risk in open-pit mining environments, helping improve safety and preparedness.

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Problem Statement

Rockfalls are a major safety and operational risk in open-pit mines. Early prediction of high-risk conditions can help prevent accidents and reduce damage to personnel and equipment.

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Solution

This project uses a Random Forest Classifier model to analyze multiple input parameters and predict rockfall risk.
Predictions are displayed through an interactive Streamlit dashboard for quick and easy decision support.

Check it out live: GeoVision360

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Key Features

• AI-powered rockfall risk prediction
• Real-time user input and instant results
• Interactive Streamlit dashboard
• End-to-end ML pipeline (training → deployment)

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Tech Stack

• Python
• Streamlit
• Pandas & NumPy
• Scikit-learn
• Matplotlib
• Joblib

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Project Structure

├── streamlit_app.py           # Streamlit application
├── rockfall_model.pkl         # Trained ML model
├── requirements.txt           # Project dependencies
└── data/                      # Input dataset

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How to Run Locally

pip install -r requirements.txt
streamlit run streamlit_app.py

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Impact

• Enhances situational awareness in mining operations
• Supports proactive safety measures
• Demonstrates practical AI application in industrial safety

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Disclaimer

This project is a hackathon prototype and should not be used for real-world safety decisions.

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