Drossel Schwabl forest fire-model

A Python + Pygame simulation of the Drossel–Schwabl Forest Fire Model, a classic example of self-organized criticality in complex systems. This project visualizes how forests grow, ignite, and burn following the principles introduced by B. Drossel and F. Schwabl (1992).

Demo

Note: The demo does not visually demonstrate emergent critical behavior — adjust variables for realistic behaviour. Grid lines are also darker due to GIFs compression.

Overview

The simulation models probabilistic forest growth and rare lightning-induced fires, featuring an interactive interface with adjustable parameters. It displays real-time forest and fire density and is built with a modular design for easy modification and experimentation.

Tech Stack

Layer	Technology
Backend / Logic	Python
Frontend / Visualization	Pygame
Core Concepts	Cellular Automata, Probabilistic Modeling, Self-Organized Criticality

Setup & Launch

Follow these steps to get the simulation running on your local machine.

1. Clone the Repository

git clone https://github.com/<your-username>/Drossel-Schwabl-forest-fire-model.git
cd Drossel-Schwabl-forest-fire-model

2. Create a Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate    # On macOS / Linux
venv\Scripts\activate       # On Windows

3. Install Dependencies

pip install pygame

4. Run the Simulation

python main.py

Usage

Key	Action
Enter	Start the simulation
Spacebar	Pause or resume the simulation
R	Restart Simulation (saves parameters)

Concepts Learned

• Implementing cellular automata for spatial simulations
• Modeling probabilistic processes and emergent behavior

Tips to observe "self criticality"

• Keep f (ignition probability) very small (e.g., 0.0001) to see realistic long-term dynamics.
• Increase g (growth rate) to accelerate forest recovery between fires.
• Try running the simulation longer — large, spontaneous fires will emerge over time without any manual tuning.

References

1. Drossel, B. & Schwabl, F. (1992). Self-Organized Critical Forest-Fire Model.
   Physical Review Letters, 69(11), 1629–1632.
   https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.69.1629

2. Bak, P., Tang, C., & Wiesenfeld, K. (1988). Self-Organized Criticality: A Model for 1/f Noise.
   Physical Review A, 38(1), 364–374.
   https://journals.aps.org/pra/abstract/10.1103/PhysRevA.38.364