Noise-resilient Mineralogical Fingerprinting of Solid Waste Sources through Anthropogenic Typomorphic Mineral Assemblages

The work presents a novel mineralogical fingerprinting approach to accurately identify the source of heavy-metal hazardous solid wastes using machine learning.

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🌟 Features

• 🔍 Interactive Web App: Input mineral phases and predict waste source in real time.
• 📊 Visual Analytics: Interactive probability bar charts and 2D similarity visualization via MDS.

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🚀 Quick Start

Prerequisites

• Python ≥ 3.8
• pip or conda

Installation

# Clone the repository
git clone https://github.com/Laaery/SWFP.git
cd FP_HMHSW

# Install dependencies
pip install -r requirements.txt

📝 Citation

If you use this code or framework in your research, please cite our paper: