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PROPAGATOR, a Cellular Automata Model for Fast Wildfire Simulations: Latest Improvements and Future Perspectives †

CIMA Research Foundation, 17100 Savona, Italy
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy
Author to whom correspondence should be addressed.
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Environ. Sci. Proc. 2022, 17(1), 60;
Published: 10 August 2022
(This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk)
The development of exhaustive wildfire management strategies is a priority, especially in Mediterranean countries where fire-prone conditions are widespread. The lack of prevention and preparedness capacities and the difficulties in rapidly sharing useful information to cope with the direct impacts on exposed people among first responders and Civil Protection Authorities (CPAs) are important issues. Extreme weather conditions characterize the main wildfire emergencies in southern EU countries [1], where the fire propagation is rapid and the authorities struggle to cope with wildfire events.
For these reasons, the use of fast operational tools in emergency response, based on reliable wildfire spread maps and efficient risk maps, is an urgent requirement for first responders and CPAs. Wildfire models can be useful in predicting the wildfire spread and helping the identification of the best firefighting strategies to be applied.
PROPAGATOR [2] is a cellular automata model which simulates wildfire spread through empirical laws that guarantee probabilistic outputs. The model requires as inputs the wind speed and direction, fine fuel moisture content, the digital elevation model and the vegetation cover of the area. The underlying grid is made up of 20 meters-square cells.
The model has been an object of continuous improvement, now being able to simulate a selection of firefighting activities and the fire spotting phenomenon. A new module allows us to compute and store the fireline intensity of the simulated front. This improvement would allow the user to quantify the severity of the fire evolution, for fast impact assessments. In order to improve the reliability of both wildfire propagation and fireline intensity estimation, satellite imagery should be adopted to refine available mapping in an on-demand fashion, rendering PROPAGATOR able to perform the quick and reliable simulation of fire spread scenarios at the European scale.

Author Contributions

Conceptualization, F.B., A.T., M.D. and P.F.; methodology, F.B., A.T., M.D. and P.F.; software, F.B. and M.D.; data curation, M.D.; writing—original draft preparation, A.T. and F.B.; writing—review and editing, A.T. and F.B.; supervision, M.D. and P.F. All authors have read and agreed to the published version of the manuscript.


This work has been funded by the MED-Star “Strategies and measures for the mitigation of fire risk in the Mediterranean area” and MEDCOOPFIRE “Cooperazione Mediterranea per la difesa delle foreste dagli incendi” projects, both in the framework of the Cross-border Cooperation Programme INTERREG “Italy-France Maritime” 2014–2020, and by the Horizon 2020-funded project SAFERS “Structured Approaches for Forest Fire Emergencies in Resilient Societies”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. de Rigo, D.; Libertà, G.; Durrant, T.; Artes, T.; San-Miguel-Ayanz, J. Forest Fire Danger Extremes in Europe under Climate Change: Variability and Uncertainty; Publication Office of the European Union: Luxembourg, 2017. [Google Scholar] [CrossRef]
  2. Trucchia, A.; D’Andrea, M.; Baghino, F.; Fiorucci, P.; Ferraris, L.; Negro, D.; Gollini, A.; Severino, M. PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator. Fire 2020, 3, 26. [Google Scholar] [CrossRef]
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MDPI and ACS Style

Baghino, F.; Trucchia, A.; D’Andrea, M.; Fiorucci, P. PROPAGATOR, a Cellular Automata Model for Fast Wildfire Simulations: Latest Improvements and Future Perspectives. Environ. Sci. Proc. 2022, 17, 60.

AMA Style

Baghino F, Trucchia A, D’Andrea M, Fiorucci P. PROPAGATOR, a Cellular Automata Model for Fast Wildfire Simulations: Latest Improvements and Future Perspectives. Environmental Sciences Proceedings. 2022; 17(1):60.

Chicago/Turabian Style

Baghino, Francesco, Andrea Trucchia, Mirko D’Andrea, and Paolo Fiorucci. 2022. "PROPAGATOR, a Cellular Automata Model for Fast Wildfire Simulations: Latest Improvements and Future Perspectives" Environmental Sciences Proceedings 17, no. 1: 60.

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