🌋 Debris Flow Doom Tracker

Real-time Monitoring & Risk Assessment for Geological Hazards

📌 Project Overview

Debris flows (massive mudslides) are among the most dangerous geological events. The Doom Tracker is a data-driven system designed to monitor rainfall thresholds, soil conditions, and terrain slope to predict and track potential debris flow events.

This project aims to provide an early-warning framework by analyzing historical landslide data and real-time triggers.

🚀 Key Features

• Rainfall Threshold Analysis: Tracks Cumulative Rainfall Intensity (I-D curves) to predict when soil reaches a breaking point.
• Geospatial Mapping: Visualizes high-risk zones based on elevation and slope data.
• Predictive Modeling: Uses Machine Learning (Random Forest/XGBoost) to classify "Safe" vs. "Doom" (Hazard) conditions.
• Real-time Alerts: System logic for triggering warnings when sensors/data cross safety limits.

🛠️ Tech Stack

• Languages: Python (Pandas, NumPy, Matplotlib)
• GIS Tools: GeoPandas, Rasterio (for mapping terrain)
• Machine Learning: Scikit-Learn, XGBoost
• Data Sources: NASA Global Landslide Catalog / USGS Rainfall Data

📂 Project Structure

├── data/               # Historical landslide & rainfall datasets
├── notebooks/          # Analysis of slope stability & triggers
├── src/                # Prediction algorithms & threshold logic
├── visualizations/     # Risk heatmaps and I-D threshold plots
└── requirements.txt    # Essential libraries

🤝 Contributing

Geologists and Data Scientists are welcome to contribute. Open an issue to discuss new data sources or algorithmic improvements.

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Developed by Akshat to help predict the unpredictable.