Wildfire Projects

This repository contains a comprehensive collection of wildfire-related research, modeling, and data analysis projects. All wildfire work has been organized into a single, structured repository for better management and collaboration.

📁 Project Structure

🔥 fire-pipe/

A comprehensive wildfire data processing and analysis pipeline with machine learning models for fire prediction and spreading analysis.

Key Features:

• Data ingestion and processing workflows
• Machine learning models (including FireBench integration)
• Visualization tools
• Downscaling capabilities
• Complete pipeline automation

Contents:

• data/ - Raw and processed wildfire datasets
• models/ - ML models and training scripts
• ingest/ - Data ingestion modules
• scripts/ - Utility and automation scripts
• viz/ - Visualization components
• downscale/ - Data downscaling tools
• features/ - Feature engineering modules
• wildfire_data_fetcher/ - Data fetching utilities

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📊 wildfire_data/

Core wildfire data management and API infrastructure for accessing and processing wildfire datasets.

Key Features:

• API endpoints for wildfire data access
• Configuration management for different data sources
• Spatial data processing
• Core data handling utilities
• Quality assurance modules

Contents:

• api.py - Main API interface
• config/ - Configuration files
• core/ - Core data processing modules
• drivers/ - Database and storage drivers
• plugins/ - Extensible plugin system
• qa/ - Quality assurance tools
• spatial/ - Spatial data handling

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📈 spreading_model/

Advanced wildfire spreading models using physics-informed neural networks (PINNs) and other machine learning approaches.

Key Features:

• Physics-informed neural network implementations
• FireBench PINN data integration
• Advanced spreading algorithms
• Model training and evaluation

Contents:

• src/ - Source code for spreading models
• src/firebench_pinn_data/ - FireBench PINN data handling
• requirements.txt - Python dependencies
• Git-tracked development history

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🌊 wildfire_spreading/

Beam pipeline implementation for wildfire spreading analysis and gradient computation.

Key Features:

• Apache Beam pipeline for distributed processing
• Gradient computation for spreading analysis
• Scalable data processing workflows

Contents:

• beam_pipeline/ - Apache Beam pipeline components
• fetch_gradients.py - Gradient computation utilities

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🚀 Getting Started

Prerequisites

• Python 3.8+
• Git
• (Optional) Apache Beam for distributed processing
• (Optional) Docker for containerized environments

Installation

1. Clone the repository:

git clone https://github.com/modorethegreat/Wildfire-Projects.git
cd Wildfire-Projects

2. Install project dependencies:

# Install core dependencies
pip install -r wildfire_data/requirements.txt

# Install spreading model dependencies
pip install -r spreading_model/requirements.txt

# Install fire-pipe dependencies
pip install -r fire-pipe/requirements.txt

3. Set up environment variables:

# Copy example configuration files and modify as needed
cp wildfire_data/config/example.yaml wildfire_data/config/local.yaml

Quick Start Examples

Data Access via API

from wildfire_data.api import WildfireAPI

# Initialize API
api = WildfireAPI()

# Fetch recent wildfire data
data = api.get_recent_wildfires(days=7)
print(f"Found {len(data)} wildfire incidents")

Run Spreading Model

cd spreading_model
python src/run_model.py --config configs/default.yaml

Execute Fire-Pipe Pipeline

cd fire-pipe
python scripts/run_pipeline.py --mode ingestion --source api

📊 Project Overview

Project	Primary Focus	Technology Stack	Status
fire-pipe	Complete data pipeline	Python, ML, Apache Beam	✅ Active
wildfire_data	Data management & API	Python, FastAPI, Spatial	✅ Active
spreading_model	ML spreading models	Python, PyTorch, PINNs	✅ Active
wildfire_spreading	Beam processing	Python, Apache Beam	✅ Active

🔧 Technical Features

Data Processing

• Multi-source data ingestion from various wildfire databases
• Spatial data handling with GIS integration
• Real-time data fetching with automated updates
• Quality assurance pipelines for data validation

Machine Learning

• Physics-informed neural networks for spreading prediction
• FireBench integration for standardized evaluation
• Feature engineering pipelines optimized for wildfire data
• Model training and evaluation frameworks

Scalability

• Apache Beam pipelines for distributed processing
• Modular architecture allowing independent component scaling
• Container-ready deployment options
• Cloud integration capabilities

📈 Research Applications

This codebase supports various wildfire research applications:

• Wildfire spreading prediction using ML and physics-based models
• Historical analysis of wildfire patterns and trends
• Real-time monitoring and early warning systems
• Risk assessment and modeling for vulnerable areas
• Climate impact studies on wildfire behavior

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 Documentation

Each subproject contains its own detailed documentation:

• Check individual README.md files in each directory
• API documentation in wildfire_data/
• Model documentation in spreading_model/
• Pipeline documentation in fire-pipe/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Related Projects

• FireBench - Wildfire benchmarking suite
• Apache Beam - Unified model for defining both batch and streaming data-parallel processing pipelines

📞 Contact

For questions or collaboration opportunities, please open an issue in this repository.

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Last Updated: January 2026
Organization: Consolidated from multiple wildfire research projects