Agent-based Modeling: Wildfire prevention simulation using agent-based modeling written in Python 3+

This project uses Mesa, an Apache2 licensed agent-based modeling (or ABM) framework in Python.

This repository allows users to simulate a wildfire in a randomly generated forest environment. The random generation is based on user settable settings from the visualisation tool provided by mesa. This simulation is created in a 2D grid of size 100x100. The firefighting agents will attempt to extinguish said fire using a strategy chosen by the user, the firefighter's success depends on the settings used in the simulation. The program also allows for sensitivity analysis with a built-in script.

Above: A screenshot of the visualisation tool provided by Mesa.

Features

• User settable settings for environment generation such as wind direction, wind strength, rivers, rain, # of firefighting agents and other parameters.
• Multiple firefighting strategies ('Go to the closest fire', 'Go to the biggest fire', 'Random movements', 'Parallel attack' and 'Indirect attack')
• Sensitivity analysis (One-factor-a-time OFAT) of the environmental settings

Running the server

Cloning the repository

To clone the repository using git, run the following command in your command line tool:

git clone https://github.com/hildobby/fire-suppression-abm.git

In order to download all the required packages

To download all the packages using pip, navigate to the repository's local directory and run the following:

pip install -r "requirements.txt"

In order to run the server with visualisation

To run the simulation with the GUI in python, run the following fromt your cloned repository's local directory:

python src/server.py

Running the Sensitivity Analysis

To run the Sensitivity analysis run the following command

python src/sensitivity_analysis/ofat_sa.py

where the built-in BatchRunner of mesa is used. More precisely, the child class BatchRunnerMP is used which allows for parallel computing. One needs to determine manually which parameters to feed to the mesa build in BatchRunner such as the wind strength, the bounds and the number of cores to use.

What remains to be worked on

• Add tests and use codecov
• Adding new types of agents
• Implementing new fire fighting methods
• Make agents able to change their method depending on the circumstances

Litterature used for the project's formulas and settings

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• Cheney, N. P., Gould, J. S., McCaw, W. L., & Anderson, W. R. (2012). Predicting fire behaviour in dry eucalypt forest in southern australia. Forest Ecology and Management, 280, 120-131. Retrieved from http://www.sciencedirect.com/science/article/pii/S0378112712003325 doi:https://doi.org/10.1016/j.foreco.2012.06.012
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• Lee, Y.-H., Fried, J., Albers, H., & Haight, R. (2012, 11). Deploying initial attack resources for wildfire suppression: Spatial coordination, budget constraints, and capacity constraints. Canadian Journal of Forest Research, 43, 56-65. doi: 10.1139/cjfr-2011-0433
• McKinney, W. (2012). Python for data analysis: Data wrangling with pandas, numpy, and ipython.” O’Reilly Media, Inc.”.
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• Nolan, R. H., Boer, M. M., Collins, L., Resco de Dios, V., Clarke, H., Jenkins, M.,... Bradstock, R. A. (2020). Causes and consequences of eastern australia’s 2019-20 season of mega-fires. Global change biology.
• Russoa, L., Vakalisb, D., & Siettos, C. (2013). Simulating the wildfire in rhodes in 2008 with a cellular automata model. CHEMICAL ENGINEERING, 35.
• Shinneman, D. J., Germino, M. J., Pilliod, D. S., Aldridge, C. L., Vaillant, N. M., & Coates, P. S (2019). The ecological uncertainty of wildfire fuel breaks: examples from the sagebrush steppe. Frontiers in Ecology and the Environment, 17 (5), 279–288.
• Williams, F. (1977). Mechanisms of fire spread. In Symposium (international) on combustion (Vol. 16, pp. 1281–1294)